Deep Learning with Python, Third Edition, Chollet F., Walson M.

Deep Learning with Python, Third Edition, Chollet F., Walson M.
     
   Thank you for purchasing the MEAP for Deep Learning with Python, Third Edition. If you are looking for a resource to learn about deep learning from scratch and to quickly become able to use this knowledge to solve real-world problems, you have found the right book. Deep Learning with Python is meant for engineers and students with a reasonable amount of Python experience, but no significant knowledge of machine learning and deep learning. It will take you all the way from basic theory to advanced practical applications.




The “deep” in “deep learning”.
Deep learning is a specific subfield of machine learning: a new take on learning representations from data that emphasizes learning successive layers of increasingly meaningful representations. The “deep” in “deep learning” isn’t a reference to any kind of deeper understanding achieved by the approach; rather, it stands for this idea of successive layers of representations. How many layers contribute to a model of the data is called the depth of the model. Other appropriate names for the field could have been layered representations learning or hierarchical representations learning. Modern deep learning often involves tens or even hundreds of successive layers of representations - and they’re all learned automatically from exposure to training data. Meanwhile, other approaches to machine learning tend to focus on learning only one or two layers of representations of the data (say, taking a pixel histogram and then applying a classification rule); hence, they’re sometimes called shallow learning.

In deep learning, these layered representations are learned via models called neural networks, structured in literal layers stacked on top of each other. The term neural network is a reference to neurobiology, but although some of the central concepts in deep learning were developed in part by drawing inspiration from our understanding of the brain (in particular the visual cortex), deep-learning models are not models of the brain. There’s no evidence that the brain implements anything like the learning mechanisms used in modern deep-learning models. You may come across pop science articles proclaiming that deep learning works like the brain or is modeled after the brain, but that isn’t the case. It would be confusing and counterproductive for newcomers to the field to think of deep learning as being in any way related to neurobiology; you don’t need that shroud of “just like our minds” mystique and mystery, and you may as well forget anything you may have read about hypothetical links between deep learning and biology. For our purposes, deep learning is a mathematical framework for learning representations from data.

Contents.
1. Welcome.
2. 1 What is deep learning?.
3. 2 The mathematical building blocks of neural networks.
4. 3 Introduction to TensorFlow, PyTorch, JAX, and Keras.
5. 4 Classification and regression.
6. 5 Fundamentals of machine learning.
7. 6 The universal workflow of machine learning.
8. 7 A deep dive on Keras.
9. 8 Image classification.
10. 9 Convnet architecture patterns.
11. 10 Interpreting what convnets learn.
12. 11 Image segmentation.
13. 12 Object detection.
14. 13 Timeseries forecasting.
15. 14 Text classification.
16. 15 Language models and the Transformer.
17. 16 Text generation.
18. 17 Image generation.
19. 18 Best practices for the real world.
20. 19 The future of AI.
21. 20 Conclusions.



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