Introduction to Deep Learning
Deep learning is said to be a subfield of machine learning which works on algorithms which are highly inspired by the structure and function of the brain. It is also useful in making weather predictions about rain, earthquakes, and tsunamis. With the help of deep learning, machines can give the required output and also help the machines to recognize people and objects.
Books on Deep Learning includes:
“Deep Learning with Python“, a book written by François Chollet is a popular book for those who are interested in understanding and implementing deep learning concepts. His firsthand knowledge and insights into deep learning and neural networks are presented in an easy-to-follow style. This book also gives an idea on hands-on approach to deep learning by the use of Python and Keras library.
The book is meant to guide readers from the basics of deep learning to more advanced topics. It begins with an introduction to neural networks and the mathematical foundations in such a way that is more understandable for readers without a strong background.
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The Deep Learning Adaptive Computation and Machine Learning series is a well-known book series which is published by MIT Press and is dedicated to advanced topics in artificial intelligence, machine learning, and computational sciences. These books are authored by leading researchers and are also used by practitioners, researchers and students with a goal to promote the unification of the many diverse strands of machine learning research in order to foster high quality research and innovative applications. This series will publish works of the highest quality that are easily understood. Research monographs, introductory and advanced level textbooks, how-to books for practitioners will all be considered at the same time.
Here are some of the key books from the series:
1.Deep Learning by Ian Goodfellow, Yoshua, and Aaron Courville
is a book on deep learning which covers a wide range of topics from basic principles to advanced techniques and is mostly used in graduate courses and also provides a comprehensive introduction to the field.
Probabilistic Machine Learning series by Kevin P. Murphy:
2.Probabilistic Machine Learning An Introduction year 2022 which focuses on foundational probabilistic models and covers Advanced Topics which is a more advanced treatment for researchers.
3.Pattern Recognition and Machine Learning by Christopher M. Bishop: This edition covers statistical pattern recognition, Bayesian networks, and neural networks and is another staple in the ML field.
4.Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams is a specialized book which focus on Gaussian processes, which are powerful tools for regression and classification of problems
Both Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, and Deep Learning with Python by François Chollet are foundational books in the field of deep learning and cater to different audiences and works for various purposes by making them “better” while depending on the needs.
The choice between “Deep Learning” by Ian Goodfellow et al. and “Deep Learning with Python” by François Chollet totally depends on the person’s background, goals and the depth of knowledge. Below is the difference between the two:
1. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville has:
- Theoretical depth and foundational principles.
- It covers the mathematical foundations of machine learning and neural networks.
- It gives knowledge about the various concepts like optimization, regularization, generative models and advanced architectures.
- It is geared toward understanding the “why” behind deep learning.
- It is best for researchers, students and professionals with a solid math and programming background.
Pros:
- It is comprehensive and rigorous.
- It covers both classical machine learning and deep learning approaches.
Cons:
- It can be overwhelming for beginners.
- It has limited hands-on coding and practical applications.
2. Deep Learning with Python by François Chollet
- It has Hands-on, practical implementation by the use of Python and Keras.
- It introduces deep learning concepts in an accessible manner.
- It provides a great focus on building and understanding neural networks with real-world examples.
- It is included with the techniques for computer vision, NLP and transfer learning.
- It is an ideal choice for beginners and intermediate practitioners who are looking to build projects quickly.
Pros:
- It is an easy-to-follow and conversational style.
- It has a strong emphasis on coding and implementation.
Cons:
- It shows less focus on theory and underlying mathematics.
- It is limited in scope as compared to the Goodfellow book.
Conclusion:
In conclusion it can be said that If anyone is looking for theoretical depth, a rigorous understanding of deep learning’s principles, and wants to do research then Deep Learning by Goodfellow is the better choice and if anyone wants to prefer a practical and project-driven approach that helps to implement deep learning models quickly then Deep Learning with Python by Chollet is best suited according to the needs.
It can also be said that a combination of both books can help to provide a comprehensive learning experience by using Deep Learning for theoretical insights and Deep Learning with Python for practical skills.