Deep Learning with PyTorch, 2Ed (Final) by Howard Huang (.PDF)
File Size: 28.8 MB
Deep Learning with PyTorch: Training and applying Deep Learning and Generative AI models, 2nd Edition (Final Release) by Howard Huang, Eli Stevens, Luca Antiga, Thomas Viehmann
Requirements: .PDF reader, 28.8 MB | True PDF
Overview: PyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models. Instantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. In this book you’ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. You’ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier. Deep Learning with PyTorch, Second Edition shows you how to build neural network models using the latest version of PyTorch. Clear explanations and practical projects help you master the fundamentals and explore advanced architectures including transformers and LLMs. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning. For Python programmers with a background in Machine Learning.
Genre: Non-Fiction > Tech & Devices
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