What are the best Books for Deep Learning?

First, there is nothing like the best book, every book has its own best parts & limitations. If you intend to learn Deep Learning & make a profession out of it then you can relate to some of the books listed below:
• “Deep Learning” by “Aaron Courville, Ian Goodfellow, and Yoshua Bengio”
An overview of a wide range of deep learning issues, including mathematical and conceptual basis, industry-based deep learning approaches, and research viewpoints.
• “Deep Learning with Python” by “François Chollet”
The following are some of the contents of the book:

  • Introduction
  • Applications of Deep Learning Techniques Next Steps
  • Sentiment Analysis using TensorFlow and Neural Networks in Practice
  • Using RNNs for Sequence Classification
  • TensorFlow Implements Sequence Classification Using RNNs
  • Deep Learning Sources & References Glossary of Some Useful Terms
  • Anaconda Setup & Python Crash Course (bonus chapter)

• “Deep Learning: A Practitioner’s Approach” by “Adam Gibson and Josh Patterson”
Even though interest in machine learning is at an all-time high, unrealistic expectations sometimes sabotage initiatives before they ever get off the ground. How might machine learning, particularly deep neural networks, help your company succeed?

• “Deep Learning for Coders with Fastai and PyTorch” by “Jeremy Howard and Sylvain Gugger”
Deep learning is sometimes thought to be the sole realm of math PhDs and large tech firms. However, as this hands-on approach reveals, Python programmers with limited math expertise, modest quantities of data, and minimum code may produce excellent results in deep learning.