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Keras vs TensorFlow vs scikit-learn: What are the differences?
Tensorflow is the most famous library in production for deep learning models. Offers automatic differentiation to perform backpropagation smoothly, allowing you to literally build any machine learning model literally. Keras 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. Scikit Learn 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.
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.
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.
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.
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.
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.
Pros of Keras
- Quality Documentation5
- Easy and fast NN prototyping4
- Supports Tensorflow and Theano backends3
Pros of scikit-learn
- Scientific computing18
- Easy13
Pros of TensorFlow
- High Performance23
- Connect Research and Production16
- Deep Flexibility13
- Auto-Differentiation9
- True Portability9
- Easy to use2
- High level abstraction2
- Powerful1
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Cons of Keras
- Hard to debug3
Cons of scikit-learn
- Limited1
Cons of TensorFlow
- Hard8
- Hard to debug5
- Documentation not very helpful1