PyTorch Zero to All
Learn essential machine learning and deep learning concepts using PyTorch, covering linear models, neural networks, CNNs, and RNNs in a comprehensive series of lectures.
PyTorch for Python 3.5
Learn PyTorch fundamentals, from linear models to advanced CNNs. Master gradient descent, back-propagation, regression, and neural network architectures for efficient deep learning implementation.
Deep Learning With PyTorch - Full Course
Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.
PyTorch Crash Course - Getting Started with Deep Learning
Comprehensive introduction to PyTorch, covering tensor basics, autograd, linear regression, neural networks, and CNNs. Ideal for beginners in deep learning.
ODSC East 2016 - Building Neural Networks at Twitter with Autograd
Explore building flexible neural networks using autograd, a tool that simplifies gradient computation and enables easy experimentation with new model architectures at Twitter.
Deep Learning and the Relational Algebra
Exploring the integration of machine learning inference in query processing, including tensor operations and autograd in relational algebra, with insights from early research experiences.
An Introduction to PyTorch & Autograd
Explore PyTorch's dynamic computation graphs and Autograd package for efficient deep learning, with practical examples and insights into GPU processing and algorithmic differentiation.