PyTorch for Python 3.5

Dive into a comprehensive 2.5-hour tutorial series on PyTorch for Python 3.5, covering essential concepts from basic linear models to advanced convolutional neural networks. Learn about gradient descent, back-propagation, autograd, and implement linear and logistic regression using PyTorch. Explore data handling with PyTorch DataLoader, understand softmax classifiers, and delve into both basic and advanced CNN architectures. Gain insights into wide and deep learning models, and wrap up with a quick 5-minute PyTorch overview. Master the fundamentals and advanced techniques of PyTorch to enhance your deep learning skills.

PyTorch Lecture 01: Overview.
PyTorch Lecture 02: Linear Model.
PyTorch Lecture 03: Gradient Descent.
PyTorch Lecture 04: Back-propagation and Autograd.
PyTorch Lecture 05: Linear Regression in the PyTorch way.
PyTorch Lecture 08: PyTorch DataLoader.
PyTorch Lecture 09: Softmax Classifier.
PyTorch Lecture 10: Basic CNN.
PyTorch Lecture 11: Advanced CNN.
PyTorch Lecture 07: Wide and Deep.
PyTorch Lecture 06: Logistic Regression.
PyTorch Lecture 08: PyTorch DataLoader.
PyTorch Lecture 09: Softmax Classifier.
PyTorch in 5 Minutes.