What are the top books to learn Deep Learning? According to ChatGPT, here are the Top 10 most read books of Deep Learning domain:
- The Hundred-Page Machine Learning Book by Andriy Burkov: This book provides a comprehensive overview of the fundamentals of machine learning, as well as practical advice for applying them to real-world problems. It is written in an easy-to-understand language and is suitable for beginners, as well as experienced professionals.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: This book provides an in-depth introduction to machine learning with Scikit-Learn and TensorFlow. It covers the basics of machine learning, along with providing practical examples for building and training models.
- Deep Learning with Python by François Chollet: This book provides an introduction to deep learning with Python. It covers the basics of deep learning, while also providing practical examples and applications of building and training deep learning models.
- Deep Learning In Python/ Pytorch by Manning Publications: This book provides an introduction to deep learning in Python, with a focus on building and training models with Pytorch. It covers the basics of deep learning and provides practical examples for building and training models.
- Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido: This book provides an introduction to machine learning with Python. It covers the fundamentals of machine learning and provides practical examples for building and training models, as well as understanding the underlying principles.
- Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow by Anirudh Koul: This book provides an introduction to deep learning for cloud, mobile, and edge computing applications. It provides practical examples of building and training models with Python, Keras, and TensorFlow.
- Machine Learning for Dummies by John Paul Mueller: This book provides a comprehensive overview of the fundamentals of machine learning, as well as practical advice for applying them to real-world problems. It is written in an easy-to-understand language and is suitable for beginners, as well as experienced professionals.
- Python for Data Analysis by Wes McKinney: This book provides an introduction to data analysis with Python. It covers the basics of data science, as well as providing practical examples for building and training data analysis models.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is the go to book for those who want to become an expert in the theory behind Deep Learning. It covers the fundamentals of the technology, including neural networks, machine learning, and artificial intelligence. It also provides practical examples of building and training deep learning models. It is written in an easy-to-understand language and is suitable for beginners, as well as experienced professionals.
- Deep Learning by IBM is a comprehensive guide to understanding and applying deep learning to businesses. It covers the basics of deep learning, as well as providing practical examples and applications of building and training deep learning models. It also provides real-world case studies that illustrate how businesses are using AI and deep learning to improve operations and gain insights. Furthermore, it is written in an easy-to-understand language and is suitable for beginners, as well as experienced professionals.
