PyTorch Zero to All

Dive into a comprehensive series of lectures covering fundamental Machine Learning and Deep Learning concepts using PyTorch. Learn about linear models, gradient descent, back-propagation, autograd, linear and logistic regression, wide and deep networks, PyTorch DataLoader, softmax classifiers, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Explore advanced topics in CNNs and RNN classification. Gain hands-on experience with PyTorch implementation throughout the course. Conclude with an introduction to NSML, a machine learning platform designed to streamline model development.

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 06: Logistic Regression.
PyTorch Lecture 07: Wide and Deep.
PyTorch Lecture 08: PyTorch DataLoader.
PyTorch Lecture 09: Softmax Classifier.
PyTorch Lecture 10: Basic CNN.
PyTorch Lecture 11: Advanced CNN.
PyTorch Lecture 12: RNN1 - Basics.
PyTorch Lecture 13: RNN 2 - Classification.
Lecture 99: NSML: A Machine Learning Platform That Enables You to Focus on Your Models.