Download PDF Deep Learning with Python Francois Chollet 9781617294433 Books
Download As PDF : Deep Learning with Python Francois Chollet 9781617294433 Books
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 François Chollet, this book builds your understanding through intuitive explanations and practical examples.
Purchase of the print book includes a free eBook in PDF, , 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 François 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.
What's Inside
- Deep learning from first principles
- Setting up your own deep-learning environment
- Image-classification models
- Deep learning for text and sequences
- Neural style transfer, text generation, and image generation
About the Reader
Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.
About the Author
François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
Table of Contents
- What is deep learning?
- Before we begin the mathematical building blocks of neural networks
- Getting started with neural networks
- Fundamentals of machine learning
- Deep learning for computer vision
- Deep learning for text and sequences
- Advanced deep-learning best practices
- Generative deep learning
- Conclusions
- appendix A - Installing Keras and its dependencies on Ubuntu
- appendix B - Running Jupyter notebooks on an EC2 GPU instance
PART 1 - FUNDAMENTALS OF DEEP LEARNING
PART 2 - DEEP LEARNING IN PRACTICE
Download PDF Deep Learning with Python Francois Chollet 9781617294433 Books
"I'm using this as the primary textbook for a Deep Learning course I'm designing right now for the University of Washington professional/continuing education program. I'll also assign readings from the Goodfellow et al. text, but Chollet's book is a more practical way to get started. He is also the author of the Keras framework; it's great to get advice "straight from the horse's mouth".
Overall this book is more about practical techniques and python code (in Keras) than about deep learning math/theory. This is probably what the majority of readers are looking for. It's a great synthesis of the most important techniques now (start of 2018), which is hard to get just from reading papers.
I would recommend complementing this book with two others:
1) as mentioned above: Deep Learning (Adaptive Computation and Machine Learning series)
2) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems"
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Deep Learning with Python Francois Chollet 9781617294433 Books Reviews :
Deep Learning with Python Francois Chollet 9781617294433 Books Reviews
- I'm a CS professor, and I chose this for my course in Deep Learning last term. Overall I am happy with the book, and will use it again. It rates 5 (or even 6!) stars for being an approachable introduction to Deep Learning, using the author's excellent Keras library to allow beginners to do remarkable work. My own class of undergrads was building DLNN models to do sophisticated image recognition tasks after just a few weeks.
So, why the four stars? Because the book is rather "paint by the numbers". The presentation is filled with "Now you'll do this.." followed by working blocks of code for the student to enter and run. But there are no exercises, code or mathematical. Even the standard backpropagation algorithm is only qualitatively described -- nice pictures of gradient descent in 2 dimensions, but no hard equations. (After all, Keras does it all for you, right?) And as the book ventures into more advanced areas like GANs, VAEs, etc the presentation is increasingly high-level and nonmathematical, providing only a feel for the topics without deep comprehension. Given the depth of the math involved, I suppose I can't blame Chollet for a bit of handwaving. But more rigor with deeper explanations would have been nice. - I'm using this as the primary textbook for a Deep Learning course I'm designing right now for the University of Washington professional/continuing education program. I'll also assign readings from the Goodfellow et al. text, but Chollet's book is a more practical way to get started. He is also the author of the Keras framework; it's great to get advice "straight from the horse's mouth".
Overall this book is more about practical techniques and python code (in Keras) than about deep learning math/theory. This is probably what the majority of readers are looking for. It's a great synthesis of the most important techniques now (start of 2018), which is hard to get just from reading papers.
I would recommend complementing this book with two others
1) as mentioned above Deep Learning (Adaptive Computation and Machine Learning series)
2) Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems - I have taken the machine learning class in Coursera but the first two chapters in this book brough a whole new level of clarity to all the concepts. I finally really get what each of the parts of the training and optimization do. Also love the explanations in code instead of in mathematics. I feel I have a much better intuition about vectors than I did before.
- After completing deeplearning.ai courses on coursera.com, I purchased this book to gain a better understanding of Keras. Keras was used in the courses, but wasn't explained so well. The author provides that explanation but also adds his perspective on neural networks and valuable insights and historical context. I don't think you get a depth of understanding for neural networks from the book. But if you already explored the field of deep learning, this is a great book to help take your exploration to the next level. I am able to use Keras more effectively to quickly try different architectures. It's great book and worth the spend.
- I cannot recommend this book highly enough. I have Geron's book on machine learning which is good but I was looking for an explanation of what is under the covers behind the python functions in tensorflow. Chollet, the author of this book, provides an excellent tutorial on the basics. He breaks down complex algorithms involving tensors to the many underlying simple calculations. I like the way he uses python notation to explain the mathematical constructs and operations rather than subscript indices found in most books. Explanations are aided by effective conceptual diagrams. I also like the way he advises when sections can be skipped if the reader has familiarity with specific topics. I find the writing highly readable.
- If you have taken some deep learning classes on Coursera, such as deeplearning.ai or fast.ai class, this book will serve as a refresher and a good tutorial to implement ideas in Keras. While it does not provide deep theoretical concepts, it explains enough to give you an understanding of what each layer does (conv1D, conv2D, LSTM, GRU, Dense, etc.) It also teaches about different ways to assemble the networks. I especially like the chapter that talks about the functional API, where you can have multiple inputs, and multiple outputs, and layer weight sharing. Most of the other books I read only talked about Sequential models. This book is not for you, if you are looking for mathematical explanations. It's perfect for someone who is not too interested in equations, and just want to have practical understanding.
- Just finished the first three chapters of this book and you can really feel the enthusiasm of the author. He put so much effort in making the book comprehensible. For example, he doesn't use math equations to explain the theory of neural network but turn to Python code instead. It proves way easier to understand for me, someone working in industry for years. He begins by going straight into our first neural network, stating that "we have to start somewhere", which is a very good philosophy. During this "going straight" process, he knows exactly when I, as a beginner, will get puzzled and always put hints at the right place in the book, telling me not to worry if I don't something. He also uses a lot of metaphors to express concepts, making it fun to read but without loss of accuracy.
This book is up-to-date and it is a masterpiece.
Will update this review as I read through the book.
