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6.105)"></rect><rect width="15.543" height="1.137" x="-1.805" y="5.3" rx=".569" transform="rotate(135 5.967 5.967)"></rect></g></svg></button></div></div><div class="css-5k0ecd"><div class="css-z3du7b"><div class="css-1s3pndt"><p><strong>Keras vs TensorFlow vs scikit-learn: What are the differences?</strong></p> <p><strong>Tensorflow</strong> is the most famous library in production for deep learning models. Offers automatic differentiation to perform backpropagation smoothly, allowing you to <em>literally</em> build any machine learning model literally. <strong>Keras</strong> is a high-level API built on Tensorflow. It is user-friendly and helps quickly build and test a neural network with minimal lines of code. Like building simple or complex neural networks within a few minutes. Modular since everything in Keras can be represented as modules. <strong>Scikit Learn</strong> is a general machine learning library built on top of NumPy. It features a lot of utilities for general pre and post-processing of data. It is a library in Python used to construct traditional models. </p> </div></div></div><div id="decisions" style="pointer-events:none;grid-column:span 3;display:block;height:182px;margin-top:-182px"></div><div style="border-bottom:none;padding-bottom:0" class="css-5k0ecd"><div class="css-oa3yz4"><div class="css-1g9kten">Decisions about Keras, scikit-learn, and TensorFlow</div><div class="css-uw1ldh"><div class="css-4gnhqq"><div class="css-xkasvz"><div class="css-atv6j6"><div class="css-1m8tyev"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div></div></div><div class="css-1vf0ygt"><div class="css-utnqcw"><div>Xiang Chen</div></div><div class="css-ds0hbm"><a href="/legendchen-x/decisions/105778342342594749" class="css-nil"><time>Feb 23, 2021</time> <!-- -->| 1 upvote · 43.4K views</a></div></div></div></div><div class="css-nil"><div class="css-dg4ww9"><svg xmlns="http://www.w3.org/2000/svg" width="21" height="21" viewBox="0 0 21 21"><g fill="none"><path 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3.428c0-.14.108-.252.248-.252h1.53c.138 0 .25.11.25.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395h.001zm0 3.429c0-.14.108-.253.248-.253h1.53a.25.25 0 0 1 .25.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824h.001zm3.717-6.857c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967zm0 3.428c0-.14.11-.252.25-.252h1.53a.25.25 0 0 1 .249.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395zm0 3.429c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824z" id="Shape" fill="#919191" fill-rule="nonzero" opacity=".404" mask="url(#mask-2)"></path></g></g><g id="d-link" transform="translate(0 17)" fill="#FFF" fill-rule="nonzero"><path d="M0 0h24v4.6A2.4 2.4 0 0 1 21.6 7H2.4A2.4 2.4 0 0 1 0 4.6V0z" id="d"></path></g><g id="Clipped" transform="translate(0 17)"><mask id="mask-4" fill="#fff"><use xlink:href="#path-3"></use></mask><rect id="Rectangle-path" fill="#E5E5E5" fill-rule="nonzero" mask="url(#mask-4)" x="5.408" y="2" width="13.521" height="1.717" rx=".859"></rect></g></g></g></svg><div itemProp="name" class="css-1b2ktnx">START</div></a><span class="css-odgfgk">)</span></div><div class="css-1dp4k20"><div class="css-ztg526"><p>Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.</p> <p>I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.</p></div></div></div><a class="css-1srb7t4">See more</a></div><div class="css-uw1ldh"><div class="css-4gnhqq"><div class="css-xkasvz"><div class="css-atv6j6"><div class="css-1m8tyev"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div></div></div><div class="css-1vf0ygt"><div class="css-utnqcw"><div>Fabian Ulmer</div></div><div class="css-ds0hbm">Software Developer<!-- --> at Hestia<!-- --> · <a href="/ulmerf/decisions/105712299385684429" class="css-nil"><time>Feb 11, 2021</time> <!-- -->| 3 upvotes · 38.7K views</a></div></div></div></div><div class="css-nil"><div class="css-dg4ww9"><svg xmlns="http://www.w3.org/2000/svg" width="21" height="21" viewBox="0 0 21 21"><g fill="none"><path fill="#068DFE" d="M20.918 19.017l-1.442-4.195a10.3 10.3 0 0 0 1.062-4.537 10.208 10.208 0 0 0-2.949-7.207A10.199 10.199 0 0 0 10.436 0a10.23 10.23 0 0 0-7.428 3.006A10.228 10.228 0 0 0 0 10.437a10.199 10.199 0 0 0 3.077 7.153 10.207 10.207 0 0 0 7.191 2.95h.016a10.3 10.3 0 0 0 4.537-1.063l4.195 1.442a1.485 1.485 0 0 0 1.543-.357c.408-.408.545-1 .358-1.544z"></path><path fill="#FFF" d="M9.644 14.658a.923.923 0 0 1-.715.342.93.93 0 0 1-.71-.338l-1.98-2.36a1.044 1.044 0 0 1 .08-1.418.914.914 0 0 1 1.342.086l1.24 1.478 4.444-5.117a.914.914 0 0 1 1.342-.072c.39.372.42 1.006.068 1.418l-5.111 5.981z"></path></g></svg><div class="css-z9ms26">Chose</div><a href="/keras" class="css-1pwtf47"><span title="Keras" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/5601/keras.png" alt="Keras" class="css-1rz3855"/></span><span class="css-i6njnm">Keras</span></a><div class="css-6uh2e7">over</div><a href="/pytorch" class="css-1pwtf47"><span title="PyTorch" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/8171/YYpjkbVn_400x400.jpg" alt="PyTorch" class="css-1rz3855"/></span><span class="css-i6njnm">PyTorch</span></a><div class="css-6uh2e7">at</div><a itemscope="" 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1-.249-.253V8.824h.001zm3.717-6.857c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967zm0 3.428c0-.14.11-.252.25-.252h1.53a.25.25 0 0 1 .249.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395zm0 3.429c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824z" id="Shape" fill="#919191" fill-rule="nonzero" opacity=".404" mask="url(#mask-2)"></path></g></g><g id="d-link" transform="translate(0 17)" fill="#FFF" fill-rule="nonzero"><path d="M0 0h24v4.6A2.4 2.4 0 0 1 21.6 7H2.4A2.4 2.4 0 0 1 0 4.6V0z" id="d"></path></g><g id="Clipped" transform="translate(0 17)"><mask id="mask-4" fill="#fff"><use xlink:href="#path-3"></use></mask><rect id="Rectangle-path" fill="#E5E5E5" fill-rule="nonzero" mask="url(#mask-4)" x="5.408" y="2" width="13.521" height="1.717" rx=".859"></rect></g></g></g></svg><div itemProp="name" class="css-1b2ktnx">Hestia</div></a><span class="css-odgfgk">)</span></div><div class="css-1dp4k20"><div class="css-ztg526"><p>For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case. </p> <p>We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice. </p></div></div></div><a class="css-1srb7t4">See more</a></div><div class="css-uw1ldh"><div class="css-4gnhqq"><div class="css-xkasvz"><div class="css-atv6j6"><div class="css-1m8tyev"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div></div></div><div class="css-1vf0ygt"><div class="css-utnqcw"><div>Xi Huang</div></div><div class="css-ds0hbm">Developer<!-- --> at University of Toronto<!-- --> · <a href="/xhuang98/decisions/105017991497244607" class="css-nil"><time>Oct 11, 2020</time> <!-- -->| 8 upvotes · 76.7K views</a></div></div></div></div><div class="css-nil"><div class="css-dg4ww9"><svg xmlns="http://www.w3.org/2000/svg" width="21" height="21" viewBox="0 0 21 21"><g fill="none"><path fill="#068DFE" d="M20.918 19.017l-1.442-4.195a10.3 10.3 0 0 0 1.062-4.537 10.208 10.208 0 0 0-2.949-7.207A10.199 10.199 0 0 0 10.436 0a10.23 10.23 0 0 0-7.428 3.006A10.228 10.228 0 0 0 0 10.437a10.199 10.199 0 0 0 3.077 7.153 10.207 10.207 0 0 0 7.191 2.95h.016a10.3 10.3 0 0 0 4.537-1.063l4.195 1.442a1.485 1.485 0 0 0 1.543-.357c.408-.408.545-1 .358-1.544z"></path><path fill="#FFF" d="M9.644 14.658a.923.923 0 0 1-.715.342.93.93 0 0 1-.71-.338l-1.98-2.36a1.044 1.044 0 0 1 .08-1.418.914.914 0 0 1 1.342.086l1.24 1.478 4.444-5.117a.914.914 0 0 1 1.342-.072c.39.372.42 1.006.068 1.418l-5.111 5.981z"></path></g></svg><div class="css-z9ms26">Chose</div><a href="/anaconda" class="css-1pwtf47"><span title="Anaconda" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/4168/vB6sRNsh_400x400.jpg" alt="Anaconda" class="css-1rz3855"/></span><span class="css-i6njnm">Anaconda</span></a><a href="/numpy" class="css-1pwtf47"><span title="NumPy" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/2179/default_332f874a2edb2686f578aa6389313efcea1eec41.png" alt="NumPy" 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1.967c0-.14.11-.253.249-.253h1.53c.138 0 .25.11.25.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967zm0 3.428c0-.14.11-.252.249-.252h1.53c.138 0 .25.11.25.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395zm0 3.429c0-.14.11-.253.249-.253h1.53a.25.25 0 0 1 .25.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824zm0 3.428c0-.14.11-.252.249-.252h1.53c.138 0 .25.11.25.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252v-1.553zM10.14 1.967c0-.14.11-.253.25-.253h1.53c.138 0 .249.11.249.253v1.552c0 .14-.109.252-.249.252h-1.53a.249.249 0 0 1-.25-.252V1.967zm0 3.428c0-.14.11-.252.25-.252h1.53c.138 0 .249.11.249.252v1.553c0 .14-.109.252-.249.252h-1.53a.249.249 0 0 1-.25-.252V5.395zm0 3.429c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.249.253h-1.53a.249.249 0 0 1-.25-.253V8.824zm0 3.428c0-.14.11-.252.25-.252h1.53c.138 0 .249.11.249.252v1.553c0 .14-.109.252-.249.252h-1.53a.249.249 0 0 1-.25-.252v-1.553zM6.423 1.967a.25.25 0 0 1 .248-.253h1.531a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.249.252h-1.53a.249.249 0 0 1-.25-.252V1.967h.001zm0 3.428c0-.14.109-.252.248-.252h1.531a.25.25 0 0 1 .249.252v1.553a.25.25 0 0 1-.249.252h-1.53a.249.249 0 0 1-.25-.252V5.395h.001zm0 3.429a.25.25 0 0 1 .248-.253h1.531a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.249.253h-1.53a.249.249 0 0 1-.25-.253V8.824h.001zm0 3.428c0-.14.109-.252.248-.252h1.531a.25.25 0 0 1 .249.252v1.553a.25.25 0 0 1-.249.252h-1.53a.249.249 0 0 1-.25-.252v-1.553h.001zM13.86 1.967c0-.14.108-.253.248-.253h1.53c.138 0 .25.11.25.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967h.001zm0 3.428c0-.14.108-.252.248-.252h1.53c.138 0 .25.11.25.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395h.001zm0 3.429c0-.14.108-.253.248-.253h1.53a.25.25 0 0 1 .25.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824h.001zm3.717-6.857c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967zm0 3.428c0-.14.11-.252.25-.252h1.53a.25.25 0 0 1 .249.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395zm0 3.429c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824z" id="Shape" fill="#919191" fill-rule="nonzero" opacity=".404" mask="url(#mask-2)"></path></g></g><g id="d-link" transform="translate(0 17)" fill="#FFF" fill-rule="nonzero"><path d="M0 0h24v4.6A2.4 2.4 0 0 1 21.6 7H2.4A2.4 2.4 0 0 1 0 4.6V0z" id="d"></path></g><g id="Clipped" transform="translate(0 17)"><mask id="mask-4" fill="#fff"><use xlink:href="#path-3"></use></mask><rect id="Rectangle-path" fill="#E5E5E5" fill-rule="nonzero" mask="url(#mask-4)" x="5.408" y="2" width="13.521" height="1.717" rx=".859"></rect></g></g></g></svg><div itemProp="name" class="css-1b2ktnx">Dtect</div></a><span class="css-odgfgk">)</span></div><div class="css-1dp4k20"><div class="css-ztg526"><p>For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.</p></div></div></div><a class="css-1srb7t4">See more</a></div><div class="css-uw1ldh"><div class="css-4gnhqq"><div class="css-xkasvz"><div class="css-atv6j6"><div class="css-1m8tyev"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div></div></div><div class="css-1vf0ygt"><div class="css-utnqcw"><div>cfvedova</div></div><div class="css-ds0hbm"><a href="/cfvedova/decisions/105012866492966270" class="css-nil"><time>Oct 10, 2020</time> <!-- -->| 3 upvotes · 54.8K views</a></div></div></div></div><div class="css-nil"><div class="css-dg4ww9"><svg xmlns="http://www.w3.org/2000/svg" width="21" height="21" viewBox="0 0 21 21"><g fill="none"><path fill="#068DFE" d="M20.918 19.017l-1.442-4.195a10.3 10.3 0 0 0 1.062-4.537 10.208 10.208 0 0 0-2.949-7.207A10.199 10.199 0 0 0 10.436 0a10.23 10.23 0 0 0-7.428 3.006A10.228 10.228 0 0 0 0 10.437a10.199 10.199 0 0 0 3.077 7.153 10.207 10.207 0 0 0 7.191 2.95h.016a10.3 10.3 0 0 0 4.537-1.063l4.195 1.442a1.485 1.485 0 0 0 1.543-.357c.408-.408.545-1 .358-1.544z"></path><path fill="#FFF" d="M9.644 14.658a.923.923 0 0 1-.715.342.93.93 0 0 1-.71-.338l-1.98-2.36a1.044 1.044 0 0 1 .08-1.418.914.914 0 0 1 1.342.086l1.24 1.478 4.444-5.117a.914.914 0 0 1 1.342-.072c.39.372.42 1.006.068 1.418l-5.111 5.981z"></path></g></svg><div class="css-z9ms26">Chose</div><a href="/matplotlib" class="css-1pwtf47"><span title="Matplotlib" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/2993/2DZC4KaA_400x400.jpg" alt="Matplotlib" class="css-1rz3855"/></span><span class="css-i6njnm">Matplotlib</span></a><a href="/numpy" class="css-1pwtf47"><span title="NumPy" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/2179/default_332f874a2edb2686f578aa6389313efcea1eec41.png" alt="NumPy" class="css-1rz3855"/></span><span class="css-i6njnm">NumPy</span></a><a href="/pandas" class="css-1pwtf47"><span title="Pandas" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/2180/1284191.png" alt="Pandas" class="css-1rz3855"/></span><span class="css-i6njnm">Pandas</span></a><a href="/python" class="css-1pwtf47"><span title="Python" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/993/pUBY5pVj.png" alt="Python" class="css-1rz3855"/></span><span class="css-i6njnm">Python</span></a><a href="/pytorch" class="css-1pwtf47"><span title="PyTorch" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/8171/YYpjkbVn_400x400.jpg" alt="PyTorch" class="css-1rz3855"/></span><span class="css-i6njnm">PyTorch</span></a><a href="/scikit-learn" class="css-1pwtf47"><span title="scikit-learn" class="css-t68yd0 service"><img src="https://img.stackshare.io/service/2657/scikit-learn-logo.png" alt="scikit-learn" class="css-1rz3855"/></span><span class="css-i6njnm">scikit-learn</span></a><div class="css-6uh2e7">at</div><a itemscope="" 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id="mask-2" fill="#fff"><use xlink:href="#path-1"></use></mask><path d="M2.704 1.967c0-.14.11-.253.249-.253h1.53c.138 0 .25.11.25.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967zm0 3.428c0-.14.11-.252.249-.252h1.53c.138 0 .25.11.25.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395zm0 3.429c0-.14.11-.253.249-.253h1.53a.25.25 0 0 1 .25.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824zm0 3.428c0-.14.11-.252.249-.252h1.53c.138 0 .25.11.25.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252v-1.553zM10.14 1.967c0-.14.11-.253.25-.253h1.53c.138 0 .249.11.249.253v1.552c0 .14-.109.252-.249.252h-1.53a.249.249 0 0 1-.25-.252V1.967zm0 3.428c0-.14.11-.252.25-.252h1.53c.138 0 .249.11.249.252v1.553c0 .14-.109.252-.249.252h-1.53a.249.249 0 0 1-.25-.252V5.395zm0 3.429c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.249.253h-1.53a.249.249 0 0 1-.25-.253V8.824zm0 3.428c0-.14.11-.252.25-.252h1.53c.138 0 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1-.249-.253V8.824h.001zm3.717-6.857c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V1.967zm0 3.428c0-.14.11-.252.25-.252h1.53a.25.25 0 0 1 .249.252v1.553a.25.25 0 0 1-.25.252h-1.53a.249.249 0 0 1-.249-.252V5.395zm0 3.429c0-.14.11-.253.25-.253h1.53a.25.25 0 0 1 .249.253v1.552a.25.25 0 0 1-.25.253h-1.53a.249.249 0 0 1-.249-.253V8.824z" id="Shape" fill="#919191" fill-rule="nonzero" opacity=".404" mask="url(#mask-2)"></path></g></g><g id="d-link" transform="translate(0 17)" fill="#FFF" fill-rule="nonzero"><path d="M0 0h24v4.6A2.4 2.4 0 0 1 21.6 7H2.4A2.4 2.4 0 0 1 0 4.6V0z" id="d"></path></g><g id="Clipped" transform="translate(0 17)"><mask id="mask-4" fill="#fff"><use xlink:href="#path-3"></use></mask><rect id="Rectangle-path" fill="#E5E5E5" fill-rule="nonzero" mask="url(#mask-4)" x="5.408" y="2" width="13.521" height="1.717" rx=".859"></rect></g></g></g></svg><div itemProp="name" class="css-1b2ktnx">Custos</div></a><span class="css-odgfgk">)</span></div><div class="css-1dp4k20"><div class="css-ztg526"><p>A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.</p></div></div></div><a class="css-1srb7t4">See more</a></div><div class="css-gyrv08"></div><div class="css-n746ki"></div></div></div><div class="css-5k0ecd"><div style="padding-top:0;padding-bottom:0" class="css-1cuq2n9"><svg class="css-1cl6o07" xmlns="http://www.w3.org/2000/svg" width="123" height="70" viewBox="0 0 123 70"><g fill="none" fill-rule="evenodd"><g fill="#008FF9" stroke="#008FF9" opacity=".74"><g stroke-width="2.207" opacity=".413"><path d="M62.27 24.009c6.068 0 11.004-4.937 11.004-11.005C73.274 6.937 68.338 2 62.27 2S51.266 6.937 51.266 13.004c0 6.068 4.936 11.005 11.004 11.005zm0-19.368c4.612 0 8.363 3.752 8.363 8.363 0 4.612-3.751 8.364-8.363 8.364-4.611 0-8.363-3.752-8.363-8.364 0-4.611 3.752-8.363 8.363-8.363zm-.579 24.58c-11.177 0-20.271 9.28-20.271 20.689 0 .73.58 1.32 1.294 1.32.714 0 1.294-.59 1.294-1.32 0-9.951 7.932-18.047 17.683-18.047 9.75 0 17.683 8.096 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0-2.056-2.056h-2.129v-1.139c0-5.021-4.014-9.11-8.953-9.11-4.938 0-8.952 4.089-8.952 9.104v1.145h-1.908a2.059 2.059 0 0 0-2.056 2.056v14.535a4.32 4.32 0 0 1-.11-.122c-3.837-4.336-7.75-11.129-7.75-20.794V27.43c0-.055.037-.103.09-.118 7.331-2.009 14.114-4.885 20.735-8.794.02-.012.04-.017.062-.017s.043.005.062.017c6.437 3.8 13.607 6.84 20.735 8.794.053.015.09.063.09.118v9.447z"></path><path fill="#FFF" d="M67.197 57.374c-.6 0-1.1-.413-1.212-.96 0 0-.032-.23 0-.463.112-.546.613-.96 1.211-.96.681 0 1.235.535 1.235 1.192 0 .657-.554 1.191-1.235 1.191m-10.759-4.22c-.68 0-1.234-.534-1.234-1.19 0-.658.553-1.192 1.234-1.192.681 0 1.235.534 1.235 1.191s-.554 1.192-1.235 1.192m9.539-5.687v-.008c.112-.546.612-.96 1.21-.96.681 0 1.235.534 1.235 1.192 0 .657-.554 1.191-1.234 1.191-.6 0-1.1-.413-1.211-.96-.028-.21 0-.455 0-.455m1.22 6.446c-1.07 0-1.972.69-2.256 1.635h-1.393l-2.175-3.612v-.006l2.172-3.605h1.386c.285.944 1.187 1.634 2.257 1.634 1.297 0 2.35-1.016 2.35-2.268 0-1.253-1.053-2.269-2.35-2.269-1.07 0-1.972.69-2.257 1.634h-2.113l-.021-.012-2.56 4.252-.02.032h-1.521c-.285-.944-1.187-1.634-2.257-1.634-1.297 0-2.35 1.016-2.35 2.268 0 1.253 1.053 2.268 2.35 2.268 1.07 0 1.972-.69 2.257-1.634h1.555l2.546 4.227.011-.007h2.134c.284.944 1.187 1.634 2.256 1.634 1.298 0 2.35-1.016 2.35-2.268 0-1.253-1.052-2.269-2.35-2.269m1.246-12.712H55.925v-1.145c0-3.511 2.806-6.364 6.26-6.364 3.452 0 6.258 2.853 6.258 6.364v1.145z"></path></g></g></svg><div class="css-172fe00">Get Advice from developers at your company using StackShare Enterprise.<!-- --> <a href="/manage-companies/enterprise" class="css-1i2jin4">Sign up for StackShare Enterprise.</a></div><a href="/enterprise" class="css-1f94cd7">Learn More</a></div></div><div id="pros" style="pointer-events:none;grid-column:span 3;display:block;height:182px;margin-top:-182px"></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>Pros of Keras</span></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>Pros of scikit-learn</span></div></div><div style="border-right:0;border-bottom:0;padding-bottom:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>Pros of TensorFlow</span></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-1v4wqws"><div class="css-nil"><div class="css-uxqild"><h2 hidden="" class="css-nil">Pros of Keras</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">7</span></div></div><span class="css-rsz8c">Easy and fast NN prototyping</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">7</span></div></div><span class="css-rsz8c">Quality Documentation</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">6</span></div></div><span class="css-rsz8c">Supports Tensorflow and Theano backends</span></div></li></ul></div></div></div></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-1v4wqws"><div class="css-nil"><div class="css-uxqild"><h2 hidden="" class="css-nil">Pros of scikit-learn</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">23</span></div></div><span class="css-rsz8c">Scientific computing</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">18</span></div></div><span class="css-rsz8c">Easy</span></div></li></ul></div></div></div></div></div><div style="border-right:0;border-bottom:0;padding-bottom:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-1v4wqws"><div class="css-nil"><div class="css-uxqild"><h2 hidden="" class="css-nil">Pros of TensorFlow</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">29</span></div></div><span class="css-rsz8c">High Performance</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">17</span></div></div><span class="css-rsz8c">Connect Research and Production</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">14</span></div></div><span class="css-rsz8c">Deep Flexibility</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">11</span></div></div><span class="css-rsz8c">Auto-Differentiation</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">10</span></div></div><span class="css-rsz8c">True Portability</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">4</span></div></div><span class="css-rsz8c">Powerful</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">4</span></div></div><span class="css-rsz8c">High level abstraction</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">4</span></div></div><span class="css-rsz8c">Easy to use</span></div></li></ul></div></div></div></div></div><div class="css-5k0ecd"><div class="css-p8n7zj"><div class="css-1uq0dwx"><svg class="css-1p6gwer" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 76 76"><g fill="none"><path fill="#068DFE" d="M73.548 63.742V23.29a6.137 6.137 0 0 0-6.129-6.129h-3.677C63.742 7.698 56.044 0 46.58 0c-9.464 0-17.162 7.698-17.162 17.161H8.581a6.136 6.136 0 0 0-6.13 6.13v40.45H0v6.13C0 73.251 2.75 76 6.129 76h63.742C73.251 76 76 73.25 76 69.871v-6.13h-2.452zm-6.129-44.13a3.682 3.682 0 0 1 3.678 3.678v40.452h-2.452V24.516c0-1.352-1.1-2.451-2.451-2.451h-3.175a17.04 17.04 0 0 0 .527-2.452h3.873zm-34.836-6.955l13.01 5.421 4.456 13.363c-1.115.27-2.272.43-3.468.43-8.112 0-14.71-6.598-14.71-14.71 0-1.571.254-3.084.712-4.504zm19.794 18.019l-4.394-13.184 9.818-9.819a14.63 14.63 0 0 1 3.49 9.488c0 6.053-3.678 11.26-8.914 13.515zM46.581 2.452a14.64 14.64 0 0 1 9.489 3.487l-9.775 9.774-12.762-5.316c2.453-4.714 7.376-7.945 13.048-7.945zM4.903 23.29a3.682 3.682 0 0 1 3.678-3.677h21.035c.12.837.289 1.657.526 2.452H9.806a2.454 2.454 0 0 0-2.451 2.451v39.226H4.903V23.29zm68.645 46.581a3.682 3.682 0 0 1-3.677 3.677H6.129a3.682 3.682 0 0 1-3.677-3.677v-3.677h25.254a9.525 9.525 0 0 0 6.407 2.451h7.775c2.39 0 4.645-.867 6.407-2.451h15.447v-2.452H47.299l-.36.36a7.096 7.096 0 0 1-5.052 2.092h-7.775a7.098 7.098 0 0 1-5.053-2.093l-.358-.36H9.806V24.517H31.1c2.762 5.789 8.653 9.807 15.482 9.807 6.828 0 12.72-4.018 15.481-9.807h4.132v41.678h7.354v3.677z"></path><path fill="#068DFE" d="M60.286 51.286c-.76 0-1.468.227-2.057.613l-4.32-3.176c.11-.344.186-.702.186-1.08 0-2.008-1.666-3.643-3.714-3.643s-3.714 1.635-3.714 3.643c0 .418.086.813.219 1.187l-6.935 5.442A3.728 3.728 0 0 0 38 53.714a3.73 3.73 0 0 0-2.215.736l-5.339-3.491c.075-.285.125-.58.125-.888 0-2.008-1.666-3.642-3.714-3.642-2.047 0-3.714 1.634-3.714 3.642 0 .309.05.603.125.888l-5.339 3.491a3.722 3.722 0 0 0-2.215-.736c-2.047 0-3.714 1.635-3.714 3.643S13.667 61 15.714 61c2.048 0 3.715-1.635 3.715-3.643 0-.308-.051-.602-.126-.887l5.34-3.492c.62.457 1.381.736 2.214.736s1.595-.279 2.215-.736l5.339 3.492c-.075.285-.125.58-.125.887C34.286 59.365 35.952 61 38 61s3.714-1.635 3.714-3.643c0-.418-.086-.813-.219-1.187l6.935-5.442a3.728 3.728 0 0 0 4.008-.055l4.319 3.176c-.11.344-.186.702-.186 1.08 0 2.008 1.667 3.642 3.715 3.642 2.047 0 3.714-1.634 3.714-3.642 0-2.009-1.667-3.643-3.714-3.643zM15.714 58.57a1.229 1.229 0 0 1-1.238-1.214c0-.669.556-1.214 1.238-1.214.682 0 1.238.545 1.238 1.214 0 .669-.556 1.214-1.238 1.214zm11.143-7.285a1.229 1.229 0 0 1-1.238-1.215c0-.668.556-1.214 1.238-1.214.682 0 1.238.546 1.238 1.214 0 .67-.556 1.215-1.238 1.215zM38 58.57a1.229 1.229 0 0 1-1.238-1.214c0-.669.556-1.214 1.238-1.214.682 0 1.238.545 1.238 1.214 0 .669-.556 1.214-1.238 1.214zm12.381-9.714a1.229 1.229 0 0 1-1.238-1.214c0-.669.556-1.214 1.238-1.214.682 0 1.238.545 1.238 1.214 0 .669-.556 1.214-1.238 1.214zm9.905 7.286a1.229 1.229 0 0 1-1.238-1.214c0-.67.556-1.215 1.238-1.215a1.23 1.23 0 0 1 1.238 1.215c0 .668-.556 1.214-1.238 1.214z"></path><path fill="#E8F4FF" d="M32 27H12v17h20z"></path><path fill="#068DFE" d="M17 32h10v2H17zm0 5h10v2H17zm18 2h2v2h-2z"></path><path fill="#E8F4FF" d="M39 39h24v2H39z"></path></g></svg><h1 class="css-lr10t7">Sign up to add or upvote pros<span class="css-4yzqpf">Make informed product decisions</span></h1></div><button data-testid="signupFullAccessButton" class="css-st9eg3">Sign up now</button></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>Cons of Keras</span></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>Cons of scikit-learn</span></div></div><div style="border-right:0;border-bottom:0;padding-bottom:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>Cons of TensorFlow</span></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-1v4wqws"><div class="css-nil"><div class="css-uxqild"><h2 hidden="" class="css-nil">Cons of Keras</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">4</span></div></div><span class="css-rsz8c">Hard to debug</span></div></li></ul></div></div></div></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-1v4wqws"><div class="css-nil"><div class="css-uxqild"><h2 hidden="" class="css-nil">Cons of scikit-learn</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">2</span></div></div><span class="css-rsz8c">Limited</span></div></li></ul></div></div></div></div></div><div style="border-right:0;border-bottom:0;padding-bottom:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-1v4wqws"><div class="css-nil"><div class="css-uxqild"><h2 hidden="" class="css-nil">Cons of TensorFlow</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">9</span></div></div><span class="css-rsz8c">Hard</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">6</span></div></div><span class="css-rsz8c">Hard to debug</span></div></li><li class="css-6nrkpz"><div class="css-1mlbi0l"><div class="css-1cda8k4"><div class="css-2an9kq"><svg style="margin-top:3px;margin-bottom:3px" xmlns="http://www.w3.org/2000/svg" width="10" height="8" viewBox="0 0 10 8"><path fill="#565656" fill-rule="evenodd" d="M5 0l5 8H0z"></path></svg><span class="css-5x5cr6">1</span></div></div><span class="css-rsz8c">Documentation not very helpful</span></div></li></ul></div></div></div></div></div><div class="css-5k0ecd"><div class="css-p8n7zj"><div class="css-1uq0dwx"><svg class="css-1p6gwer" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 76 76"><g fill="none"><path fill="#068DFE" d="M73.548 63.742V23.29a6.137 6.137 0 0 0-6.129-6.129h-3.677C63.742 7.698 56.044 0 46.58 0c-9.464 0-17.162 7.698-17.162 17.161H8.581a6.136 6.136 0 0 0-6.13 6.13v40.45H0v6.13C0 73.251 2.75 76 6.129 76h63.742C73.251 76 76 73.25 76 69.871v-6.13h-2.452zm-6.129-44.13a3.682 3.682 0 0 1 3.678 3.678v40.452h-2.452V24.516c0-1.352-1.1-2.451-2.451-2.451h-3.175a17.04 17.04 0 0 0 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offset 0" class="css-3vlw85"><div class="css-acfjt6"><div class="css-w707sh"></div></div></div><div data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-acfjt6"><div class="css-w707sh"></div></div></div><div style="border-right:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-acfjt6"><div class="css-w707sh"></div></div></div><div data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-14bxxd1"><div class="css-1bshyi4">- No public GitHub repository available -</div></div></div><div data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-14bxxd1"><a href="https://github.com/scikit-learn/scikit-learn" target="_blank" class="css-1hlwa6q"><svg class="css-f9zb2m" xmlns="http://www.w3.org/2000/svg" width="50" height="48" viewBox="0 0 50 48"><path fill="#161514" fill-rule="evenodd" d="M0 24.608C0 35.48 7.163 44.703 17.097 47.956c1.25.227 1.707-.533 1.707-1.185 0-.584-.022-2.132-.034-4.185-6.954 1.487-8.421-3.3-8.421-3.3-1.138-2.842-2.777-3.599-2.777-3.599-2.27-1.526.172-1.495.172-1.495 2.51.173 3.83 2.536 3.83 2.536 2.23 3.76 5.851 2.674 7.276 2.044.227-1.59.873-2.674 1.586-3.289-5.55-.62-11.387-2.733-11.387-12.162 0-2.686.974-4.884 2.574-6.603-.258-.622-1.116-3.125.245-6.512 0 0 2.098-.662 6.874 2.523 1.994-.547 4.133-.82 6.259-.83 2.124.01 4.262.283 6.258.83 4.773-3.185 6.868-2.523 6.868-2.523 1.365 3.387.507 5.89.25 6.512 1.603 1.72 2.57 3.917 2.57 6.603 0 9.454-5.846 11.534-11.414 12.143.896.76 1.696 2.26 1.696 4.557 0 3.289-.031 5.943-.031 6.75 0 .658.451 1.424 1.719 1.184C42.844 44.693 50 35.477 50 24.608 50 11.017 38.806 0 24.998 0 11.194 0 0 11.017 0 24.608z" opacity=".35"></path></svg><div class="css-1wdjt5z"><div class="css-w707sh"></div></div></a></div></div><div style="border-right:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-14bxxd1"><a href="https://github.com/tensorflow/tensorflow" target="_blank" class="css-1hlwa6q"><svg class="css-f9zb2m" xmlns="http://www.w3.org/2000/svg" width="50" height="48" viewBox="0 0 50 48"><path fill="#161514" fill-rule="evenodd" d="M0 24.608C0 35.48 7.163 44.703 17.097 47.956c1.25.227 1.707-.533 1.707-1.185 0-.584-.022-2.132-.034-4.185-6.954 1.487-8.421-3.3-8.421-3.3-1.138-2.842-2.777-3.599-2.777-3.599-2.27-1.526.172-1.495.172-1.495 2.51.173 3.83 2.536 3.83 2.536 2.23 3.76 5.851 2.674 7.276 2.044.227-1.59.873-2.674 1.586-3.289-5.55-.62-11.387-2.733-11.387-12.162 0-2.686.974-4.884 2.574-6.603-.258-.622-1.116-3.125.245-6.512 0 0 2.098-.662 6.874 2.523 1.994-.547 4.133-.82 6.259-.83 2.124.01 4.262.283 6.258.83 4.773-3.185 6.868-2.523 6.868-2.523 1.365 3.387.507 5.89.25 6.512 1.603 1.72 2.57 3.917 2.57 6.603 0 9.454-5.846 11.534-11.414 12.143.896.76 1.696 2.26 1.696 4.557 0 3.289-.031 5.943-.031 6.75 0 .658.451 1.424 1.719 1.184C42.844 44.693 50 35.477 50 24.608 50 11.017 38.806 0 24.998 0 11.194 0 0 11.017 0 24.608z" opacity=".35"></path></svg><div class="css-1wdjt5z"><div class="css-w707sh"></div></div></a></div></div><div id="description" style="pointer-events:none;grid-column:span 3;display:block;height:182px;margin-top:-182px"></div><div data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><h2 class="css-i52n91">What is Keras?</h2><div class="css-13sfqhu">Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/</div></div></div><div data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><h2 class="css-i52n91">What is scikit-learn?</h2><div class="css-13sfqhu">scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.</div></div></div><div style="border-right:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><h2 class="css-i52n91">What is TensorFlow?</h2><div class="css-13sfqhu">TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.</div></div></div><div class="css-5k0ecd"><div data-testid="getAdvice" class="css-1ke07jm"><div class="css-1di6wpo"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div><h1 class="css-lr10t7">Need advice about which tool to choose?<span class="css-4yzqpf">Ask the StackShare community!</span></h1></div><button class="css-st9eg3">Get Advice</button></div></div><div class="css-5k0ecd"><div id="jobs" style="pointer-events:none;grid-column:span 3;display:block;height:182px;margin-top:-182px"></div><div class="css-f8hv9w"><span>Jobs that mention Keras, scikit-learn, and TensorFlow as a desired skillset</span></div><div class="css-i5u9v5"><div class="css-1ewdw7x"><div class="css-ih8ay1"><a href="/companies/pinterest" title="Pinterest&#x27;s company profile"><div class="lazyload-wrapper"><div 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title="Pinterest&#x27;s company profile" class="css-6mpj06">Pinterest</a><div class="css-nio1yx"><svg xmlns="http://www.w3.org/2000/svg" width="11" height="14" viewBox="0 0 11 14"><g fill="none" fill-rule="evenodd" stroke="#49A8F9"><path d="M4.5 12.205l.013.017c.492.612 1.243.612 1.738-.017a25.074 25.074 0 0 0 1.789-2.37c.475-.717.873-1.41 1.171-2.06.376-.818.575-1.534.575-2.11 0-2.418-1.974-4.38-4.41-4.38-2.437 0-4.411 1.962-4.411 4.38 0 .576.2 1.292.575 2.11.298.65.696 1.343 1.17 2.06a25.074 25.074 0 0 0 1.79 2.37z"></path><ellipse cx="5.375" cy="5.697" rx="1.732" ry="1.732"></ellipse></g></svg><span class="css-ak2nbp">San Francisco, CA, US; </span></div></div><div class="css-9yycaw"><a data-testid="jobsCta" title="Machine Learning Engineer, Search Quality" href="https://www.pinterestcareers.com/job-form?gh_jid=4559950" target="_blank" rel="noreferrer noopener nofollow" class="css-leu1cr">View Job Details</a><div class="css-156fhm0"><div class="css-12alag6"><span 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1.789-2.37c.475-.717.873-1.41 1.171-2.06.376-.818.575-1.534.575-2.11 0-2.418-1.974-4.38-4.41-4.38-2.437 0-4.411 1.962-4.411 4.38 0 .576.2 1.292.575 2.11.298.65.696 1.343 1.17 2.06a25.074 25.074 0 0 0 1.79 2.37z"></path><ellipse cx="5.375" cy="5.697" rx="1.732" ry="1.732"></ellipse></g></svg><span class="css-ak2nbp">Toronto, ON, CA</span></div></div><div class="css-9yycaw"><a data-testid="jobsCta" title="Machine Learning Engineer, Content Quality Signals" href="https://www.pinterestcareers.com/job-form?gh_jid=2265981" target="_blank" rel="noreferrer noopener nofollow" class="css-leu1cr">View Job Details</a><div class="css-156fhm0"><div class="css-12alag6"><span class="css-10hy8cb"><a class="css-1pwtf47"><span title="Dream" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/40514/default_c19d22a0eb52861815d6674d4209c48b26039df7.png" alt="Dream" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="PyTorch" class="css-13fg6qi service"><img 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US</span></div></div><div class="css-9yycaw"><a data-testid="jobsCta" title="Software Engineer, Ads Infrastructure " href="https://www.pinterestcareers.com/job-form?gh_jid=4176049" target="_blank" rel="noreferrer noopener nofollow" class="css-leu1cr">View Job Details</a><div class="css-156fhm0"><div class="css-12alag6"><span class="css-10hy8cb"><a class="css-1pwtf47"><span title="Infra" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/40791/default_a4357de5c49d215b8aed95cbec211f1cf5885911.jpg" alt="Infra" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="Dream" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/40514/default_c19d22a0eb52861815d6674d4209c48b26039df7.png" alt="Dream" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="PyTorch" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/8171/YYpjkbVn_400x400.jpg" alt="PyTorch" class="css-4lwqz5"/></span></a></span><span class="css-pvufaf">+<!-- -->10</span></div><svg xmlns="http://www.w3.org/2000/svg" width="14" height="12" viewBox="0 0 14 12"><g fill="none" fill-rule="evenodd"><rect width="13" height="9" x=".5" y="2.5" stroke="#A1A1A1" rx="2"></rect><path stroke="#A1A1A1" d="M4.5 2.5h5V.999A.492.492 0 0 0 9.01.5H4.99a.5.5 0 0 0-.49.499V2.5z"></path><path fill="#A1A1A1" d="M1 7h12v1H1z"></path><path stroke="#A1A1A1" d="M5.5 7.5v1.501c0 .277.225.499.51.499h1.98a.5.5 0 0 0 .51-.499V7.5h-3z"></path></g></svg></div></div></div></div><div class="css-f7loz8"><div><a data-testid="seeJobsForLink" href="/jobs/keras" title="Keras jobs" class="css-1xexurq">See jobs for Keras</a></div><div><a data-testid="seeJobsForLink" href="/jobs/scikit-learn" title="scikit-learn jobs" class="css-1xexurq">See jobs for scikit-learn</a></div><div><a data-testid="seeJobsForLink" href="/jobs/tensorflow" title="TensorFlow jobs" class="css-1xexurq">See jobs for TensorFlow</a></div></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>What companies use Keras?</span></div></div><div style="border-bottom:0;padding-bottom:0" data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>What companies use scikit-learn?</span></div></div><div style="border-right:0;border-bottom:0;padding-bottom:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-f8hv9w"><span>What companies use TensorFlow?</span></div></div><div data-notes="index 0 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-nil"><div class="css-4pt7vy"><h2 hidden="" class="css-nil">What companies use Keras?</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><a href="/delivery-hero-se/delivery-hero" class="css-mta8ak"><div class="css-1pwtf47"><span title="Delivery Hero SE" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/31891/thumb_569a11dcc8e5c89606f6f115dbe7547952a366d5.png" alt="Delivery Hero SE" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Delivery Hero SE</span></a></li><li class="css-6nrkpz"><a href="/hepsiburada/hepsiburada" class="css-mta8ak"><div class="css-1pwtf47"><span title="Hepsiburada" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/29765/thumb_11753097.png" alt="Hepsiburada" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Hepsiburada</span></a></li><li class="css-6nrkpz"><a href="/ruangguru/ruangguru" class="css-mta8ak"><div class="css-1pwtf47"><span title="Ruangguru" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/38463/thumb_98512a69e65e323d3657e3896ba78735ebb8d2d8.png" alt="Ruangguru" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Ruangguru</span></a></li><li class="css-6nrkpz"><a href="/bagelcode/bagelcode" class="css-mta8ak"><div class="css-1pwtf47"><span title="Bagelcode" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/29356/thumb_1cddbffe9cb2df4f01e2c35b221d744e7d2eae23.png" alt="Bagelcode" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Bagelcode</span></a></li><li class="css-6nrkpz"><a href="/wongnai/wongnai" class="css-mta8ak"><div class="css-1pwtf47"><span title="Wongnai" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/11920/thumb_10824508235f881f6c3f731752673ce47fadadbc.png" alt="Wongnai" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Wongnai</span></a></li><li class="css-6nrkpz"><a href="/foretag/labs" class="css-mta8ak"><div class="css-1pwtf47"><span title="Foretag" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/28545/thumb_7b8416bda665ec4be5b4d5a4a1535ce1a814c707.png" alt="Foretag" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Foretag</span></a></li><li class="css-6nrkpz"><a href="/upfluence/upfluence" class="css-mta8ak"><div class="css-1pwtf47"><span title="Upfluence" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/30338/thumb_1a0e9d7ef4f37363e96a1466863c5860d157768a.jpg" alt="Upfluence" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Upfluence</span></a></li></ul></div></div></div></div><div data-notes="index 1 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-nil"><div class="css-4pt7vy"><h2 hidden="" class="css-nil">What companies use scikit-learn?</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><a href="/delivery-hero-se/delivery-hero" class="css-mta8ak"><div class="css-1pwtf47"><span title="Delivery Hero SE" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/31891/thumb_569a11dcc8e5c89606f6f115dbe7547952a366d5.png" alt="Delivery Hero SE" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Delivery Hero SE</span></a></li><li class="css-6nrkpz"><a href="/tokopedia/tokopedia" class="css-mta8ak"><div class="css-1pwtf47"><span title="Tokopedia" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/21102/thumb_1d5a3094fef7ba1a5b1a57f1651369581bc22cc4.jpg" alt="Tokopedia" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Tokopedia</span></a></li><li class="css-6nrkpz"><a href="/postclick/postclick" class="css-mta8ak"><div class="css-1pwtf47"><span title="Postclick" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/162124/thumb_9a859427fe3da1b99d734baa4332d66455eeddec.png" alt="Postclick" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Postclick</span></a></li><li class="css-6nrkpz"><a href="/ratepay/ratepay-gmbh" class="css-mta8ak"><div class="css-1pwtf47"><span title="Ratepay GmbH" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/30919/thumb_27465ece488d48d9d7fc5ba8043d794b00b36aa0.png" alt="Ratepay GmbH" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Ratepay GmbH</span></a></li><li class="css-6nrkpz"><a href="/sword-health/sword-health" class="css-mta8ak"><div class="css-1pwtf47"><span title="SWORD Health" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/159954/thumb_96a1ade430e6e0862c30b2e597c9d62d9b179425.jpg" alt="SWORD Health" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">SWORD Health</span></a></li><li class="css-6nrkpz"><a href="/big-insight/bigin" class="css-mta8ak"><div class="css-1pwtf47"><span title="Big Insight" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/33658/thumb_e5eb5d46666cf26a47c6836ac8ebcc0fa6a30e5b.png" alt="Big Insight" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Big Insight</span></a></li><li class="css-6nrkpz"><a href="/jodel/platform" class="css-mta8ak"><div class="css-1pwtf47"><span title="Jodel" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/28454/thumb_373bd781df2776a029e72a6519fbab7d484c89e8.png" alt="Jodel" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Jodel</span></a></li></ul></div></div></div></div><div style="border-right:0" data-notes="index 2 num 3 offset 0" class="css-3vlw85"><div class="css-nil"><div class="css-nil"><div class="css-4pt7vy"><h2 hidden="" class="css-nil">What companies use TensorFlow?</h2><ul class="css-7c9av6"><li class="css-6nrkpz"><a href="/uber-technologies/uber" class="css-mta8ak"><div class="css-1pwtf47"><span title="Uber Technologies" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/363/thumb_e5d5db9bd2df8488fe8e61913e700c52ec9c13e4.jpg" alt="Uber Technologies" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Uber Technologies</span></a></li><li class="css-6nrkpz"><a href="/delivery-hero-se/delivery-hero" class="css-mta8ak"><div class="css-1pwtf47"><span title="Delivery Hero SE" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/31891/thumb_569a11dcc8e5c89606f6f115dbe7547952a366d5.png" alt="Delivery Hero SE" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Delivery Hero SE</span></a></li><li class="css-6nrkpz"><a href="/hepsiburada/hepsiburada" class="css-mta8ak"><div class="css-1pwtf47"><span title="Hepsiburada" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/29765/thumb_11753097.png" alt="Hepsiburada" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">Hepsiburada</span></a></li><li class="css-6nrkpz"><a href="/upstageai/upstageai" class="css-mta8ak"><div class="css-1pwtf47"><span title="UpstageAI" class="css-13fg6qi service"><img src="https://img.stackshare.io/company/165580/thumb_7a2cf4e45e6d1022f9f4c672165f65f84dc51ed1.jpeg" alt="UpstageAI" class="css-4lwqz5"/></span> </div><span class="css-rsz8c">UpstageAI</span></a></li><li class="css-6nrkpz"><a href="/moneylion/technology" class="css-mta8ak"><div class="css-1pwtf47"><span title="MoneyLion" class="css-13fg6qi service"><img 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3.677H6.129a3.682 3.682 0 0 1-3.677-3.677v-3.677h25.254a9.525 9.525 0 0 0 6.407 2.451h7.775c2.39 0 4.645-.867 6.407-2.451h15.447v-2.452H47.299l-.36.36a7.096 7.096 0 0 1-5.052 2.092h-7.775a7.098 7.098 0 0 1-5.053-2.093l-.358-.36H9.806V24.517H31.1c2.762 5.789 8.653 9.807 15.482 9.807 6.828 0 12.72-4.018 15.481-9.807h4.132v41.678h7.354v3.677z"></path><path fill="#068DFE" d="M60.286 51.286c-.76 0-1.468.227-2.057.613l-4.32-3.176c.11-.344.186-.702.186-1.08 0-2.008-1.666-3.643-3.714-3.643s-3.714 1.635-3.714 3.643c0 .418.086.813.219 1.187l-6.935 5.442A3.728 3.728 0 0 0 38 53.714a3.73 3.73 0 0 0-2.215.736l-5.339-3.491c.075-.285.125-.58.125-.888 0-2.008-1.666-3.642-3.714-3.642-2.047 0-3.714 1.634-3.714 3.642 0 .309.05.603.125.888l-5.339 3.491a3.722 3.722 0 0 0-2.215-.736c-2.047 0-3.714 1.635-3.714 3.643S13.667 61 15.714 61c2.048 0 3.715-1.635 3.715-3.643 0-.308-.051-.602-.126-.887l5.34-3.492c.62.457 1.381.736 2.214.736s1.595-.279 2.215-.736l5.339 3.492c-.075.285-.125.58-.125.887C34.286 59.365 35.952 61 38 61s3.714-1.635 3.714-3.643c0-.418-.086-.813-.219-1.187l6.935-5.442a3.728 3.728 0 0 0 4.008-.055l4.319 3.176c-.11.344-.186.702-.186 1.08 0 2.008 1.667 3.642 3.715 3.642 2.047 0 3.714-1.634 3.714-3.642 0-2.009-1.667-3.643-3.714-3.643zM15.714 58.57a1.229 1.229 0 0 1-1.238-1.214c0-.669.556-1.214 1.238-1.214.682 0 1.238.545 1.238 1.214 0 .669-.556 1.214-1.238 1.214zm11.143-7.285a1.229 1.229 0 0 1-1.238-1.215c0-.668.556-1.214 1.238-1.214.682 0 1.238.546 1.238 1.214 0 .67-.556 1.215-1.238 1.215zM38 58.57a1.229 1.229 0 0 1-1.238-1.214c0-.669.556-1.214 1.238-1.214.682 0 1.238.545 1.238 1.214 0 .669-.556 1.214-1.238 1.214zm12.381-9.714a1.229 1.229 0 0 1-1.238-1.214c0-.669.556-1.214 1.238-1.214.682 0 1.238.545 1.238 1.214 0 .669-.556 1.214-1.238 1.214zm9.905 7.286a1.229 1.229 0 0 1-1.238-1.214c0-.67.556-1.215 1.238-1.215a1.23 1.23 0 0 1 1.238 1.215c0 .668-.556 1.214-1.238 1.214z"></path><path fill="#E8F4FF" d="M32 27H12v17h20z"></path><path fill="#068DFE" d="M17 32h10v2H17zm0 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0-3.85-.802-5.627-2.14a15.262 15.262 0 0 1-1.7-1.494 11.3 11.3 0 0 1-.696-.766.684.684 0 0 1 0-.862c.142-.176.379-.443.696-.766a15.262 15.262 0 0 1 1.7-1.494C5.536 1.282 7.433.48 9.386.48c1.953 0 3.85.802 5.628 2.14.625.471 1.195.98 1.7 1.494.317.323.553.59.695.766l-.289.233.289-.233zm-2.842-1.666c-1.66-1.25-3.413-1.991-5.18-1.991-1.768 0-3.522.741-5.181 1.991C3.2 3.971 2.429 4.751 1.97 5.311c.458.56 1.23 1.34 2.235 2.097 1.66 1.25 3.413 1.99 5.18 1.99 1.768 0 3.521-.74 5.18-1.99 1.005-.757 1.778-1.537 2.236-2.097-.458-.56-1.23-1.34-2.235-2.097zm2.553 1.9l-.289.233.29-.234zm-.289.16l-.03.037.03.036a.059.059 0 0 1 0-.072z"></path><path d="M9.386 7.54c-1.255 0-2.276-1-2.276-2.229 0-1.23 1.021-2.23 2.276-2.23s2.276 1 2.276 2.23-1.02 2.23-2.276 2.23m0-5.097c-1.613 0-2.926 1.286-2.926 2.867 0 1.58 1.313 2.867 2.926 2.867 1.614 0 2.927-1.286 2.927-2.867 0-1.58-1.313-2.867-2.927-2.867"></path><path d="M9.386 3.718a.322.322 0 0 0-.325.318c0 .177.146.319.325.319.538 0 .976.43.976.956a.967.967 0 0 1-.976.956.967.967 0 0 1-.975-.956.322.322 0 0 0-.325-.319.322.322 0 0 0-.326.319c0 .878.73 1.593 1.626 1.593.897 0 1.626-.715 1.626-1.593s-.73-1.593-1.626-1.593"></path></g></svg><span>6221</span></div></div></div></div><div class="css-1gtwocl"><div class="css-1kcdhc3"><div class="css-cuzej8"><div class="css-12h9n7t"><a href="/red-hat-inc/ai-slash-ml-pipelines-using-open-data-hub-and-kubeflow-on-red-hat-openshift">AI/ML Pipelines Using Open Data Hub and Kubeflow on Red Hat Op...</a></div></div><div class="css-m2gwm">Jan 29 2020 at 2:08PM</div><div class="css-14kdfdp"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div><p>Red Hat, Inc.</p></div></div><div class="css-lscypq"><div class="css-12alag6"><span class="css-10hy8cb"><a class="css-1pwtf47"><span title="Python" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/993/pUBY5pVj.png" alt="Python" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="Docker" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/586/n4u37v9t_400x400.png" alt="Docker" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="Kubernetes" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/1885/21_d3cvM.png" alt="Kubernetes" class="css-4lwqz5"/></span></a></span><span class="css-pvufaf">+<!-- -->14</span></div><div class="css-maw68b"><div class="favourites"><svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 14 14"><path fill="none" fill-rule="evenodd" stroke="#ADADAD" stroke-width="1.166" d="M12.045 6.038H8.038l-.137-.389-1.296-3.673L5.17 6.038H1.165L4.488 8.65l-.13.389-1.284 3.853 3.53-2.59.346.253 3.185 2.337L8.721 8.65l.323-.254 3-2.358zm-11.58-.55zm12.28 0z"></path></svg> <span>11</span></div><div class="views"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="11" viewBox="0 0 18 11"><g fill="#ADADAD" fill-rule="evenodd"><path fill-rule="nonzero" stroke="#ADADAD" stroke-width=".533" d="M17.409 4.88a.684.684 0 0 1 0 .862c-.142.176-.378.443-.695.766a15.262 15.262 0 0 1-1.7 1.494c-1.777 1.338-3.675 2.14-5.628 2.14-1.953 0-3.85-.802-5.627-2.14a15.262 15.262 0 0 1-1.7-1.494 11.3 11.3 0 0 1-.696-.766.684.684 0 0 1 0-.862c.142-.176.379-.443.696-.766a15.262 15.262 0 0 1 1.7-1.494C5.536 1.282 7.433.48 9.386.48c1.953 0 3.85.802 5.628 2.14.625.471 1.195.98 1.7 1.494.317.323.553.59.695.766l-.289.233.289-.233zm-2.842-1.666c-1.66-1.25-3.413-1.991-5.18-1.991-1.768 0-3.522.741-5.181 1.991C3.2 3.971 2.429 4.751 1.97 5.311c.458.56 1.23 1.34 2.235 2.097 1.66 1.25 3.413 1.99 5.18 1.99 1.768 0 3.521-.74 5.18-1.99 1.005-.757 1.778-1.537 2.236-2.097-.458-.56-1.23-1.34-2.235-2.097zm2.553 1.9l-.289.233.29-.234zm-.289.16l-.03.037.03.036a.059.059 0 0 1 0-.072z"></path><path d="M9.386 7.54c-1.255 0-2.276-1-2.276-2.229 0-1.23 1.021-2.23 2.276-2.23s2.276 1 2.276 2.23-1.02 2.23-2.276 2.23m0-5.097c-1.613 0-2.926 1.286-2.926 2.867 0 1.58 1.313 2.867 2.926 2.867 1.614 0 2.927-1.286 2.927-2.867 0-1.58-1.313-2.867-2.927-2.867"></path><path d="M9.386 3.718a.322.322 0 0 0-.325.318c0 .177.146.319.325.319.538 0 .976.43.976.956a.967.967 0 0 1-.976.956.967.967 0 0 1-.975-.956.322.322 0 0 0-.325-.319.322.322 0 0 0-.326.319c0 .878.73 1.593 1.626 1.593.897 0 1.626-.715 1.626-1.593s-.73-1.593-1.626-1.593"></path></g></svg><span>2253</span></div></div></div></div><div class="css-1gtwocl"><div class="css-1kcdhc3"><div class="css-cuzej8"><div class="css-12h9n7t"><a href="/pinterest/building-a-kubernetes-platform-at-pinterest">Building a Kubernetes Platform at Pinterest</a></div></div><div class="css-m2gwm">Dec 4 2019 at 8:01PM</div><div class="css-14kdfdp"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div><p>Pinterest</p></div></div><div class="css-lscypq"><div class="css-12alag6"><span class="css-10hy8cb"><a class="css-1pwtf47"><span title="Jenkins" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/670/jenkins.png" alt="Jenkins" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="Kubernetes" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/1885/21_d3cvM.png" alt="Kubernetes" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="TensorFlow" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/4717/FtFnqC38_400x400.png" alt="TensorFlow" class="css-4lwqz5"/></span></a></span><span class="css-pvufaf">+<!-- -->4</span></div><div class="css-maw68b"><div class="favourites"><svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 14 14"><path fill="none" fill-rule="evenodd" stroke="#ADADAD" stroke-width="1.166" d="M12.045 6.038H8.038l-.137-.389-1.296-3.673L5.17 6.038H1.165L4.488 8.65l-.13.389-1.284 3.853 3.53-2.59.346.253 3.185 2.337L8.721 8.65l.323-.254 3-2.358zm-11.58-.55zm12.28 0z"></path></svg> <span>5</span></div><div class="views"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="11" viewBox="0 0 18 11"><g fill="#ADADAD" fill-rule="evenodd"><path fill-rule="nonzero" stroke="#ADADAD" stroke-width=".533" d="M17.409 4.88a.684.684 0 0 1 0 .862c-.142.176-.378.443-.695.766a15.262 15.262 0 0 1-1.7 1.494c-1.777 1.338-3.675 2.14-5.628 2.14-1.953 0-3.85-.802-5.627-2.14a15.262 15.262 0 0 1-1.7-1.494 11.3 11.3 0 0 1-.696-.766.684.684 0 0 1 0-.862c.142-.176.379-.443.696-.766a15.262 15.262 0 0 1 1.7-1.494C5.536 1.282 7.433.48 9.386.48c1.953 0 3.85.802 5.628 2.14.625.471 1.195.98 1.7 1.494.317.323.553.59.695.766l-.289.233.289-.233zm-2.842-1.666c-1.66-1.25-3.413-1.991-5.18-1.991-1.768 0-3.522.741-5.181 1.991C3.2 3.971 2.429 4.751 1.97 5.311c.458.56 1.23 1.34 2.235 2.097 1.66 1.25 3.413 1.99 5.18 1.99 1.768 0 3.521-.74 5.18-1.99 1.005-.757 1.778-1.537 2.236-2.097-.458-.56-1.23-1.34-2.235-2.097zm2.553 1.9l-.289.233.29-.234zm-.289.16l-.03.037.03.036a.059.059 0 0 1 0-.072z"></path><path d="M9.386 7.54c-1.255 0-2.276-1-2.276-2.229 0-1.23 1.021-2.23 2.276-2.23s2.276 1 2.276 2.23-1.02 2.23-2.276 2.23m0-5.097c-1.613 0-2.926 1.286-2.926 2.867 0 1.58 1.313 2.867 2.926 2.867 1.614 0 2.927-1.286 2.927-2.867 0-1.58-1.313-2.867-2.927-2.867"></path><path d="M9.386 3.718a.322.322 0 0 0-.325.318c0 .177.146.319.325.319.538 0 .976.43.976.956a.967.967 0 0 1-.976.956.967.967 0 0 1-.975-.956.322.322 0 0 0-.325-.319.322.322 0 0 0-.326.319c0 .878.73 1.593 1.626 1.593.897 0 1.626-.715 1.626-1.593s-.73-1.593-1.626-1.593"></path></g></svg><span>3059</span></div></div></div></div><div class="css-1gtwocl"><div class="css-1kcdhc3"><div class="css-cuzej8"><div class="css-12h9n7t"><a href="/stream/stream-and-go-news-feeds-for-over-300-million-end-users">Stream & Go: News Feeds for Over 300 Million End Users</a></div></div><div class="css-m2gwm">Jan 18 2018 at 7:43AM</div><div class="css-14kdfdp"><div class="lazyload-wrapper"><div class="lazyload-placeholder"></div></div><p>Stream</p></div></div><div class="css-lscypq"><div class="css-12alag6"><span class="css-10hy8cb"><a class="css-1pwtf47"><span title="GitHub" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/27/default_869c34d29acc794d60ecdd5d2b5bfc042a80a4ec.jpg" alt="GitHub" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="Python" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/993/pUBY5pVj.png" alt="Python" class="css-4lwqz5"/></span></a><a class="css-1pwtf47"><span title="React" class="css-13fg6qi service"><img src="https://img.stackshare.io/service/1020/OYIaJ1KK.png" alt="React" class="css-4lwqz5"/></span></a></span><span class="css-pvufaf">+<!-- -->42</span></div><div class="css-maw68b"><div class="favourites"><svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 14 14"><path fill="none" fill-rule="evenodd" stroke="#ADADAD" stroke-width="1.166" d="M12.045 6.038H8.038l-.137-.389-1.296-3.673L5.17 6.038H1.165L4.488 8.65l-.13.389-1.284 3.853 3.53-2.59.346.253 3.185 2.337L8.721 8.65l.323-.254 3-2.358zm-11.58-.55zm12.28 0z"></path></svg> <span>48</span></div><div class="views"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="11" viewBox="0 0 18 11"><g fill="#ADADAD" fill-rule="evenodd"><path fill-rule="nonzero" stroke="#ADADAD" stroke-width=".533" d="M17.409 4.88a.684.684 0 0 1 0 .862c-.142.176-.378.443-.695.766a15.262 15.262 0 0 1-1.7 1.494c-1.777 1.338-3.675 2.14-5.628 2.14-1.953 0-3.85-.802-5.627-2.14a15.262 15.262 0 0 1-1.7-1.494 11.3 11.3 0 0 1-.696-.766.684.684 0 0 1 0-.862c.142-.176.379-.443.696-.766a15.262 15.262 0 0 1 1.7-1.494C5.536 1.282 7.433.48 9.386.48c1.953 0 3.85.802 5.628 2.14.625.471 1.195.98 1.7 1.494.317.323.553.59.695.766l-.289.233.289-.233zm-2.842-1.666c-1.66-1.25-3.413-1.991-5.18-1.991-1.768 0-3.522.741-5.181 1.991C3.2 3.971 2.429 4.751 1.97 5.311c.458.56 1.23 1.34 2.235 2.097 1.66 1.25 3.413 1.99 5.18 1.99 1.768 0 3.521-.74 5.18-1.99 1.005-.757 1.778-1.537 2.236-2.097-.458-.56-1.23-1.34-2.235-2.097zm2.553 1.9l-.289.233.29-.234zm-.289.16l-.03.037.03.036a.059.059 0 0 1 0-.072z"></path><path d="M9.386 7.54c-1.255 0-2.276-1-2.276-2.229 0-1.23 1.021-2.23 2.276-2.23s2.276 1 2.276 2.23-1.02 2.23-2.276 2.23m0-5.097c-1.613 0-2.926 1.286-2.926 2.867 0 1.58 1.313 2.867 2.926 2.867 1.614 0 2.927-1.286 2.927-2.867 0-1.58-1.313-2.867-2.927-2.867"></path><path d="M9.386 3.718a.322.322 0 0 0-.325.318c0 .177.146.319.325.319.538 0 .976.43.976.956a.967.967 0 0 1-.976.956.967.967 0 0 1-.975-.956.322.322 0 0 0-.325-.319.322.322 0 0 0-.326.319c0 .878.73 1.593 1.626 1.593.897 0 1.626-.715 1.626-1.593s-.73-1.593-1.626-1.593"></path></g></svg><span>40074</span></div></div></div></div></div><div class="css-1i8b05s"></div></div><div id="alternatives" style="pointer-events:none;grid-column:span 3;display:block;height:182px;margin-top:-182px"></div><div style="border-bottom:none" class="css-5k0ecd"><div class="css-4d5oui"><div class="css-ptavxb">What are some alternatives to Keras, scikit-learn, and TensorFlow?</div><div class="css-10pc7td"><div class="css-1ukyolz"><div class="css-1uackhz">PyTorch</div></div><div class="css-1ofkegx">PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.</div><div class="css-1ukyolz"><div class="css-1uackhz">MXNet</div></div><div class="css-1ofkegx">A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.</div><div class="css-1ukyolz"><div class="css-1uackhz">CUDA</div></div><div class="css-1ofkegx">A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.</div><div class="css-1ukyolz"><div class="css-1uackhz">Kubeflow</div></div><div class="css-1ofkegx">The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. </div><div class="css-1ukyolz"><div class="css-1uackhz">MLflow</div></div><div class="css-1ofkegx">MLflow is an open source platform for managing the end-to-end machine learning lifecycle.</div></div><a data-testid="seeAllAlternativesLink" href="/tensorflow/alternatives" class="css-1xexurq">See all alternatives</a></div></div></div><aside class="css-1cqgl9p"><div class="css-p223ph"><div style="position:sticky;top:23%"><div class="css-81jah4"></div><div class="css-cn7d1w"><svg class="css-1fj3l7d" xmlns="http://www.w3.org/2000/svg" width="123" height="70" viewBox="0 0 123 70"><g fill="none" fill-rule="evenodd"><g fill="#008FF9" stroke="#008FF9" opacity=".74"><g stroke-width="2.207" opacity=".413"><path d="M62.27 24.009c6.068 0 11.004-4.937 11.004-11.005C73.274 6.937 68.338 2 62.27 2S51.266 6.937 51.266 13.004c0 6.068 4.936 11.005 11.004 11.005zm0-19.368c4.612 0 8.363 3.752 8.363 8.363 0 4.612-3.751 8.364-8.363 8.364-4.611 0-8.363-3.752-8.363-8.364 0-4.611 3.752-8.363 8.363-8.363zm-.579 24.58c-11.177 0-20.271 9.28-20.271 20.689 0 .73.58 1.32 1.294 1.32.714 0 1.294-.59 1.294-1.32 0-9.951 7.932-18.047 17.683-18.047 9.75 0 17.683 8.096 17.683 18.047 0 .73.58 1.32 1.294 1.32.714 0 1.294-.59 1.294-1.32 0-11.407-9.093-20.688-20.271-20.688z" opacity=".502"></path></g><g opacity=".266"><path stroke-width="1.927" d="M33.506 27.484c-5.323 0-9.653-4.33-9.653-9.653s4.33-9.653 9.653-9.653c5.322 0 9.653 4.33 9.653 9.653 0 5.322-4.33 9.653-9.653 9.653zm0-16.99c-4.045 0-7.336 3.292-7.336 7.337s3.29 7.336 7.336 7.336c4.045 0 7.336-3.291 7.336-7.336s-3.291-7.336-7.336-7.336z" opacity=".502"></path><path stroke-width="1.986" d="M42.973 33.722c9.802 2.263 15.898 12.243 13.588 22.246a1.176 1.176 0 0 1-1.402.896 1.176 1.176 0 0 1-.867-1.42c2.015-8.726-3.303-17.432-11.853-19.406-8.55-1.974-17.146 3.52-19.161 12.246a1.177 1.177 0 0 1-1.402.896 1.176 1.176 0 0 1-.867-1.42c2.31-10.003 12.162-16.3 21.964-14.038z" opacity=".502"></path></g><g opacity=".266"><path stroke-width="1.927" d="M90.263 27.484c5.323 0 9.653-4.33 9.653-9.653s-4.33-9.653-9.653-9.653c-5.322 0-9.653 4.33-9.653 9.653 0 5.322 4.33 9.653 9.653 9.653zm0-16.99c4.045 0 7.337 3.292 7.337 7.337s-3.292 7.336-7.337 7.336-7.336-3.291-7.336-7.336 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