Hey folks! I wanted to let you know about this awesome set of books I found on AI, machine learning, and deep learning. There’s even stuff about the math behind ML.
Most of these are from O’Reilly. They’ve been super helpful for my studies and projects. I thought they might be useful for others too.
I know buying tech books can be expensive. So this is great for those on a tight budget. But if you can afford it, please support the authors and buy the books.
Here’s a quick rundown of what you’ll find:
Intro to AI concepts
Machine learning basics and advanced topics
Deep learning techniques
Math for ML and data science
Happy learning everyone! Let me know if you find any gems in there.
thx for sharing! ive been lookin for good ML resources. quick q - do these books cover practical stuff or is it all theory? i’m more into hands-on learning. also, any recommendations for someone just starting out with AI? cheers!
As someone who’s delved into these resources, I can attest to their value. They cover both theory and practical applications, which is crucial for a well-rounded understanding. For beginners, I’d recommend starting with ‘Introduction to Machine Learning with Python’ by Müller and Guido. It provides a solid foundation and includes hands-on examples.
One aspect I particularly appreciate is the inclusion of mathematics behind ML. Understanding the underlying principles has significantly improved my model implementations and debugging skills. Don’t skip the math sections, even if they seem daunting at first.
For those interested in deep learning, ‘Deep Learning with Python’ by Chollet is an excellent resource. It strikes a good balance between theory and practical implementation using Keras.
Remember, consistency is key when learning these complex topics. Set aside regular study time and work through the examples provided in the books.
Great find, DancingFox! I’ve been using these resources for a while now, and they’ve been a game-changer for my AI projects. The O’Reilly books are top-notch, especially for getting a solid grasp on the fundamentals.
One thing I’d add is that while these books are fantastic, don’t forget to supplement your learning with hands-on projects. I found that building small AI models and tackling Kaggle competitions really helped cement the concepts for me.
Also, for those diving into deep learning, make sure to check out the sections on neural network architectures. Understanding the differences between CNNs, RNNs, and transformers can really boost your ability to choose the right tool for the job.
Lastly, don’t get discouraged if some concepts seem tough at first. It took me a while to wrap my head around backpropagation, but persistence pays off. Keep at it, and you’ll see progress!