Natural Language Processing With Python

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1. FullStackDev1Apr 2023
   I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way

   Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here's a detailed learning plan, broken down into four stages:

   Stage 1: Building a Strong Foundation

   Mathematics:

   Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang

   Calculus: "Calculus: Early Transcendentals" by James Stewart

   Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

   Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

   Programming:

   Python: "Python Crash Course" by Eric Matthes

   Git: "Pro Git" by Scott Chacon and Ben Straub

   Machine Learning & Data Science Basics:

   "Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas

   Stage 2: Learning Machine Learning and Deep Learning

   Online Courses:

   Coursera: "Machine Learning" by Andrew Ng

   Coursera: "Deep Learning Specialization" by Andrew Ng

   Fast.ai: "Practical Deep Learning for Coders"

   Books:

   "Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   Stage 3: Expanding Knowledge of AI Subfields

   Natural Language Processing (NLP):

   "Speech and Language Processing" by Daniel Jurafsky and James H. Martin

   "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

   Computer Vision:

   "Computer Vision: Algorithms and Applications" by Richard Szeliski

   "Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:

   "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

   "Deep Reinforcement Learning Hands-On" by Maxim Lapan

   Stage 4: Staying Current and Gaining Practical Experience

   Read research papers:

   Subscribe to the arXiv mailing list in your areas of interest Regularly read papers from conferences like NeurIPS, ICML, and ACL Participate in online competitions:

   Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio AIcrowd: Another platform for AI competitions Contribute to open-source projects:

   TensorFlow, PyTorch, or other popular AI frameworks Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation Network with AI professionals:

   Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/MachineLearning Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!

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2. avipeltzJan 2023
   - AIMA by Russel and Norvig is a classic but I would say is more of overview of the field and for most topic areas isn't quite deep enough imo.

   - For deep learning specifically, a more applied text that is beautifully written and chock full of examples is Francois Chollet's Deep Learning with Python (there a new second edition out with up to date examples using modern versions of Tensorflow). The first 3 chapters I would give as required reading for anyone interested in understanding some deep learning fundamentals.

   - Deep Learning - goodfellow and bengio - seems like it would be hard to get through without a reading group not exactly a APUE or K&R type reading experience but I haven't spent enough time with it.

   If you haven't taken a Linear Algebra or Differential Equations class its useful stuff to know for ML/DL theory but not fully necessary to do applied work with modern high level libraries, but definitely having a strong understanding of basic matrix math is useful.

   If you have interests in natural language processing theres a couple good books:

   - Natural Language Processing with Python - Bird Klein, Loper, is a great intro to NLP concepts and working with NLTK which may be a bit dated to some but I would definitely recommend, and its online for free. Great examples.(https://www.nltk.org/book/)

   - Speech and Language Processing - Dan Jurafsky and James H. Martin - is good, though I have only spent much time with the pre-print

   And then theres a lot of papers that are good reads. Let me know if you have any questions or want a list of good papers.

   If you just want to get off the ground and start playing with stuff and building things I'd recommend fast.ai's free online course - its pretty high level and a lot is abstracted away but its a great start and can enable you to build lots of cool things pretty rapidly. Andrew Ng's online course also is quite requitable and will probably give you a bit more background and fundamentals.

   If I were to choose one book from the bunch it would be Chollet it gives you pretty much all the building blocks you need to be able to read some papers and try to implement things yourself and I find building things a much more satisfying way to learn than sitting down and writing proofs or just taking notes but thats just my preference.

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3. ineedasernameNov 2017
   I'd recommend python and the NLTK package, which has a corresponding book [0] that will bring you up to speed on the major points without skimping on the conceptual details along the way. And there's a lot of good work going on in python, like pyTorch & TensorFlow.

   But anyone looking to do serious work that skips the type of content covered in NLTK Book in favor of jumping in to DNNs is doing themself & their ambitions an extreme disservice.

   [0] http://www.nltk.org/book/

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4. sixdimensionalAug 2017
   NLTK in Python - it even has a basic example showing natural language to SQL translation that is pretty cool. Simplistic, but a good starting point for learning!

   http://www.nltk.org/book/ch10.html

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5. opportuneJun 2017
   I know NLTK isn't used for learning by itself, but it's such an essential player in the realm of NLP+Python that I think it should definitely be included (and that the list would be incomplete without it). The best link is probably http://www.nltk.org/book/ which is an excellent place to get started.

   You should probably also include the gensim package https://radimrehurek.com/gensim/, since it has the most popular python word2vec implementation. One of the links I clicked on mentions it and walks you through using it, but I think it would make sense to point people to it directly in case they don't have time for a tutorial.

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6. sprobertsonApr 2017
   Here are some NLP classes:

   * Oxford Deep NLP Lectures https://github.com/oxford-cs-deepnlp-2017/lectures

   * Stanford Natural Language Processing with Deep Learning http://web.stanford.edu/class/cs224n/syllabus.html

   * Georgia Tech Natural Language Understanding https://github.com/jacobeisenstein/gt-nlp-class

   * Georgia Tech Deep Learning For NLP in PyTorch https://github.com/rguthrie3/DeepLearningForNLPInPytorch

   And some books:

   * Natural Language Processing with Python https://www.amazon.com/Natural-Language-Processing-Python-An...

   * Foundations of Statistical Natural Language Processing https://www.amazon.com/Foundations-Statistical-Natural-Langu...

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