"Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks" is a book that covers deep learning using the Python programming language. The book was written by Lazy Programmer Inc., which is a team of experienced data scientists and machine learning practitioners.
The book is divided into four parts:
Introduction to Deep Learning: This section covers the basics of deep learning, including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). It also covers the use of deep learning for image and text analysis.
Modern Neural Networks: This section covers more advanced deep learning algorithms, such as generative adversarial networks (GANs), deep reinforcement learning, and deep belief networks (DBNs). It also covers the use of transfer learning for building deep learning models.
Applied Deep Learning: This section covers the use of deep learning in specific domains, such as computer vision, natural language processing, and speech recognition. It also covers the use of deep learning for time series analysis and anomaly detection.
Advanced Topics in Deep Learning: This section covers advanced topics in deep learning, such as autoencoders, variational autoencoders (VAEs), and deep Q-networks (DQNs). It also covers the use of deep learning for recommendation systems and hyperparameter optimization.
The book provides practical examples and code snippets using popular Python libraries for deep learning, such as TensorFlow and Keras. It also covers best practices for building deep learning models, such as data preparation, model tuning, and error analysis.
"Deep Learning in Python" is suitable for data scientists, machine learning practitioners, and anyone interested in learning about deep learning using Python. The book assumes some familiarity with Python programming and basic machine learning concepts.
