What is NLTK and what are its top alternatives?
NLTK (Natural Language Toolkit) is a widely used platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. One of its key features is the extensive range of language processing tools available, making it a go-to choice for many developers. However, NLTK can be slow for large datasets and lacks advanced deep learning capabilities compared to newer tools in the market.
- spaCy: spaCy is a fast and efficient NLP library for Python that offers pre-trained models for text processing tasks such as named entity recognition, part-of-speech tagging, and dependency parsing. It is known for its high performance and scalability, making it a popular choice for building production-ready applications. However, spaCy may have a steeper learning curve compared to NLTK.
- Gensim: Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large text collections. It offers implementations of popular algorithms like word2vec and doc2vec for word and document embeddings, making it a powerful tool for semantic analysis. Compared to NLTK, Gensim is more focused on unsupervised learning tasks.
- Stanford NLP: Stanford NLP provides a suite of NLP tools developed by the Stanford NLP Group, including named entity recognition, sentiment analysis, and dependency parsing. It is known for its accuracy and robustness, especially in tasks like entity linking and coreference resolution. However, setting up and integrating Stanford NLP can be more complex compared to NLTK.
- Flair: Flair is a simple and powerful tool for NLP in Python, offering state-of-the-art embeddings and pre-trained models for text classification, named entity recognition, and part-of-speech tagging. It also provides easy-to-use APIs for training custom models on new datasets. Compared to NLTK, Flair focuses more on deep learning techniques for NLP tasks.
- TextBlob: TextBlob is a user-friendly NLP library for processing textual data in Python, offering simple APIs for common NLP tasks like sentiment analysis, part-of-speech tagging, and noun phrase extraction. It also provides access to WordNet for semantic analysis. TextBlob is easier to learn and use compared to NLTK, making it suitable for beginners in NLP.
- AllenNLP: AllenNLP is a deep learning framework for NLP tasks built on top of PyTorch, providing modular components for building state-of-the-art models in areas like text classification, question answering, and language modeling. It offers easy experimentation with different architectures and datasets, but may require more expertise in deep learning compared to NLTK.
- Hugging Face Transformers: Transformers by Hugging Face is a popular library for pre-trained NLP models, including BERT, GPT-2, and RoBERTa, that can be easily fine-tuned for downstream tasks like text classification and language generation. It offers a wide range of models and tools for working with transformers, making it a cutting-edge alternative to NLTK for deep learning-based NLP.
- FastText: FastText is a library for efficient learning of word embeddings and text classification, developed by Facebook Research. It provides fast training and inference for word representations and text categorization tasks, especially for large-scale datasets. Compared to NLTK, FastText is optimized for performance and scalability in NLP applications.
- BERT: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained NLP model developed by Google AI that achieved state-of-the-art results across various NLP benchmarks. It offers fine-tuning capabilities for downstream tasks like question answering and named entity recognition, making it a powerful alternative to NLTK for advanced NLP projects.
- Spacy Transformers: Spacy Transformers is an integration of spaCy and transformers models for easy usage of pre-trained transformer-based models in spaCy pipelines. It allows for seamless integration of transformer models for tasks like text classification, entity recognition, and summarization, offering a modern approach to NLP compared to traditional rule-based methods like NLTK.
Top Alternatives to NLTK
SpaCyIt is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages. ...
GensimIt is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. ...
TensorFlowTensorFlow 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. ...
PyTorchPyTorch 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. ...
scikit-learnscikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. ...
KerasDeep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...
CUDAA 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. ...
StreamlitIt is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API. ...
NLTK alternatives & related posts
- Speed12
- No vendor lock-in2
- Requires creating a training set and managing training1
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Can you please advise which one to choose FastText Or Gensim, in terms of:
- Operability with ML Ops tools such as MLflow, Kubeflow, etc.
- Performance
- Customization of Intermediate steps
- FastText and Gensim both have the same underlying libraries
- Use cases each one tries to solve
- Unsupervised Vs Supervised dimensions
- Ease of Use.
Please mention any other points that I may have missed here.
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Hard9
- Hard to debug6
- Documentation not very helpful2
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.
Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.
!
- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
- Lots of code3
- It eats poop1
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Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
- Scientific computing25
- Easy19
- Limited2
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Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?
Hi, I wanted to jump into Machine Learning.
I first tried brain.js, but its capabilities are very limited and it abstracts most concepts of ML away. I've tried TensorFlow, but it's very hard for me to understand the concepts.
Now, I thought about trying NumPy or scikit-learn, but I don't really know much about ML, but still want to use 100% Power of ML.
What do you recommend me to use as a beginner in ML?
Also do you know any good tutorials which explain how ML works and how to implement it in a given framework (ideal in german)?
Thanks for your attention & help :D
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
- Hard to debug4
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.
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Streamlit
- Fast development10
- Fast development and apprenticeship1