Deep Learning with PyTorch 1.x: Implement deep learning techniques and neural network architecture variants using Python

Provides a comprehensive guide to implementing deep learning techniques and neural network architectures using Python and the PyTorch framework. Authored by Laura Mitchell Sri. Yogesh K. and Vishnu Subramanian, it covers foundational concepts like neural network structure, tensors, and training processes, progressing to advanced topics such as computer vision using convolutional neural networks (CNNs) and transfer learning. The text further explores natural language processing (NLP), including text tokenization, word embeddings, and recurrent neural networks (RNNs) like LSTMs. Additionally, it introduces unsupervised learning through autoencoders, restricted Boltzmann machines (RBMs), generative adversarial networks (GANs), and deep reinforcement learning concepts like Deep Q-Networks (DQNs) and policy gradient methods.

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Deep Learning with PyTorch 1.x: Implement deep learning techniques and neural network architecture variants using Python

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