Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION: Updated all code to TensorFlow 2Introduced the high-level Keras APINew and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more.
Stats for nerds
8recommend
16mentions
12people
2019–2024years
63% recommend31% neutral6% critical
Mentions per year
What Hacker News says
- Lots of good suggestions here already. I'd start by adding one quick note though. "AI" is more than just LLM's. Sure, the "current, trendy, fashionable" thing is all LLM's, but the field as a whole is still much larger. I'd encourage you to not myopically focus on LLM's to exclusion. Depending on your existing background knowledge, there's a lot to be said for going out and getting a copy of Artificial Intelligence: A Modern Approach and reading through it. Likewise for something like Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow.
Beyond that: there are some decent sub-reddits for keeping up with AI happenings, a lot of good Youtube channels (although a lot of the ones that talk about the "current, trendy" AI stuff tend to be a bit tabloid'ish), and even a couple of Facebook groups. You can also find good signal by choosing the right people to follow on Twitter/LinkedIn/Mastodon/Bluesky/etc.
https://www.reddit.com/r/artificial/
https://reddit.com/r/machineLearning/
https://www.reddit.com/r/ollama/
https://www.youtube.com/@matthew_berman
https://www.youtube.com/@TheAiGrid
https://www.youtube.com/@WesRoth
https://www.youtube.com/@DaveShap
https://www.youtube.com/c/MachineLearningStreetTalk
https://www.youtube.com/@twimlai
https://www.youtube.com/@YannicKilcher
And you can always go straight to "the source" and follow pre-prints showing up in arXiv.
For tools to make it easier to track new releases, arXiv supports subscriptions to daily digest emails, and also has RSS feeds.
https://info.arxiv.org/help/subscribe.html
https://info.arxiv.org/help/rss.html
There are also some bots in the Fediverse that push out links to new arXiv papers.
- Hi Friend -
This question was recently asked in the /r/machinelearning subreddit.
You can find the link here: https://www.reddit.com/r/learnmachinelearning/comments/18yzv...
Generally the first question I ask when someone asks me that question is:
What are you trying to do?
If you are learning machine learning to do research, your answer will be very different than if you are learning machine learning to start a business or create something with the ML tools/applications available today.
General knowledge ML book:
* Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition by Aurélien Géron
General knowledge data applications book:
* Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems 1st Edition by Martin Kleppmann
General knowledge statistical machine learning book:
* The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition by Trevor Hastie (Author), Robert Tibshirani (Author), Jerome Friedman (Author)
Tooling wise, the rabbit hole goes very deep depending on what technologies and what areas you are exploring. Anything from PyTorch all the way to programming GPUs and how to store / work with massive data as well as general algos to work with all of this.
- Would you recommend it?
Which book would you recommend, a book with practical approach, comprehensive and readable for latest developments in dl?
Some other recommendations include
"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd ed." by Aurélien Géron (2022), published by O'Reilly, is one of my favorite technical books ever. It was published before LLMs became hot.
- Studying C or Python is studying C or Python. Rather, study AI. Pick up python along the way, skip C.
https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQ...
https://www.youtube.com/watch?v=jGwO_UgTS7I&ab_channel=Stanf...
There's tons of book, but I recommend this since it's hands on "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...
https://developers.google.com/machine-learning/crash-course
download some local LLMs and play with them along the way. Good luck.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019) by Aurélien Géron is very good, both covering theory and presenting code.
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