Deep Learning with Python

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Master the practical aspects of implementing deep learning solutions with PyTorch through a hands-on approach that combines theory and practice. This updated edition prepares you to tackle real-world problems with a solid theoretical foundation and practical skills using PyTorch, developed by Facebook's AI Research Group. Begin with an overview of how deep learning with PyTorch has become a revolutionary framework equipped with tools to address real-world challenges. You'll delve into the mathematical fundamentals of linear algebra, vector calculus, probability, and optimization. After establishing this groundwork, explore key components of PyTorch, including layers, loss functions, and optimization algorithms, while gaining insights into GPU-based computation crucial for training deep learning models. The book covers essential architectures such as feedforward networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, autoencoders, and generative adversarial networks. With practical tips for training and optimizing deep learning models, this edition explains best practices for deploying these models in production with PyTorch. You will review machine learning concepts like overfitting, underfitting, and regularization, understand deep learning fundamentals, apply linear algebra with PyTorch, and work on tuning and optimizing models.