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Python Deep Learning
Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
Daniel Slater , Gianmario Spacagna , Ivan Vasilev , Peter Roelants , Valentino Zocca
Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries
Key Features- Build a strong foundation in neural networks and deep learning with Python libraries
- Explore advanced deep learning techniques and their applications across computer vision and NLP
- Learn how a computer can navigate in complex environments with reinforcement learning
- Grasp the mathematical theory behind neural networks and deep learning processes
- Investigate and resolve computer vision challenges using convolutional networks and capsule networks
- Solve generative tasks using variational autoencoders and Generative Adversarial Networks
- Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
- Explore reinforcement learning and understand how agents behave in a complex environment
- Get up to date with applications of deep learning in autonomous vehicles
This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.
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