Deep Learning with Python
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran'ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran'ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
Stats for nerds
36recommend
64mentions
53people
2016–2026years
70% recommend28% neutral2% critical
Mentions per year
What Hacker News says
- I like Karpathy, we come from the same lineage and I am very proud of him for what he's accomplished, he's a very impressive guy.
In regards to deep learning, building deep learning architecture is one of my greatest joys in finding insights from perceptual data. Right now, I'm working on spatiotemporal data modeling to build prediction systems for urban planning to improve public transportation systems. I build ML infrastructure too and plan to release an app that deploys the model in the wild within event streams of transit systems.
It took me a month to master the basics and I've spent a lot of time with online learning, with Deeplearning.ai and skills.google. Deeplearning.ai is ok, but I felt the concepts a bit dated. The ML path at skills.google is excellent and gives a practical understanding of ML infrastructure, optimization and how to work with gpus and tpus (15x faster than gpus).
But the best source of learning for me personally and makes me a confident practitioner is the book by Francois Chollet, the creator of Keras. His book, "Deep Learning with Python", really removed any ambiguity I've had about deep learning and AI in general. Francois is extremely generous in how he explains how deep learning works, over the backdrop of 70 years of deep learning research. Francois keeps it updated and the third revision was made in September 2025 - its available online for free if you don't want to pay for it. He gives you the recipe for building a GPT and Diffusion models, but starts from the ground floor basics of tensor operations and computation graphs. I would go through it again from start to finish, it is so well written and enjoyable to follow.
The most important lesson he discusses is that "Deep learning is more of an art than a science". To get something working takes a good amount of practice and the results on how things work can't always be explained.
He includes notebooks with detailed code examples with Tensorflow, Pytorch and Jax as back ends.
Deep learning is a great skill to have. After reading this book, I can recreate scientific abstracts and deploy the models into production systems. I am very grateful to have these skills and I encourage anyone with deep curiosity like me to go all in on deep learning.
- Here is the bible on deep learning, ”Deep learning with Python” written by Francois Chollet, the creator of Keras.
- From one François to an other, thank you for you work, and all the best with your next endeavor!
Your various tutorials and your book "Deep Learning with Python" have been invaluable in helping me get up to speed in applied deep learning and in learning the ropes of a field I knew nothing about.
- I recommend _Deep Learning with Python_ by François Chollet (the creator of Keras). It’s very clear and approachable, explains all of these concepts, and doesn’t try to “impress” you with unnecessary mathematical notation. Excellent introductory book.
The only downside is that in 2024, you are probably going to use PyTorch and not Keras + Tensorflow as shown in the book.
- Read Deep Learning with Python [1]. It's ok not to understand everything, but doing the labs will be invaluable.
That book should set you up with the fundamentals. Pytorch is the defacto standard right now for training; during deployment you will use whatever your deployment setup allows (e.g. embedded devices typically have their own inference frameworks).
Python is the lingua franca for anything in machine learning, but other languages are used where necessary for performance or for ecosystem benefits.
[1]: https://www.manning.com/books/deep-learning-with-python
- You say you have no experience with deep learning. Start by learning about "traditional" deep learning models, then move on to LLMs. Learning LLMs before deep learning is like learning algebra before addition.
"Deep Learning with Python, Second Edition" by Google's François Chollet is a pretty good intro, and also touches on LLMs / generative models towards the end.
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