Thomas Viehmann Books
Thomas Viehmann is a machine learning researcher and PyTorch contributor.
Known for: Deep Learning with PyTorch
Books by Thomas Viehmann
Deep Learning with PyTorch
Deep Learning with PyTorch introduces the PyTorch framework and guides readers through building deep learning models from scratch. It covers fundamental concepts such as tensors, automatic differentiation, and neural network design, progressing to advanced topics like generative models and deployment. The book emphasizes practical implementation and understanding of deep learning principles using real-world examples.
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Tensors and the Language of Deep Learning
Every concept in deep learning rests upon one elemental construct: the tensor. In PyTorch, tensors are more than arrays — they are living numerical entities that can move across CPUs and GPUs, support automatic differentiation, and form the foundation for all computations. Understanding tensors is l...
From Deep Learning with PyTorch
Autograd: Teaching Machines to Learn
Learning in neural networks depends on a simple but profound idea: optimization through gradient descent. PyTorch’s autograd system turns this mathematical abstraction into an effortless computational process. When you perform operations on tensors with `requires_grad=True`, PyTorch automatically bu...
From Deep Learning with PyTorch
About Thomas Viehmann
Thomas Viehmann is a machine learning researcher and PyTorch contributor.
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Thomas Viehmann is a machine learning researcher and PyTorch contributor.
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