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PyTorch Zero to All

via YouTube

Overview

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This course aims to teach learners the fundamentals of Machine Learning and Deep Learning using PyTorch. By the end of the course, students will be able to understand and implement linear models, gradient descent, back-propagation, autograd, linear regression, logistic regression, wide and deep models, data loading with PyTorch DataLoader, softmax classifiers, basic and advanced Convolutional Neural Networks (CNNs), as well as Recurrent Neural Networks (RNNs) for classification. The teaching method involves a series of lectures covering each topic sequentially. This course is designed for individuals interested in starting their journey in Machine Learning and Deep Learning using PyTorch, with a focus on understanding the core concepts and practical implementation of various models.

Syllabus

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.

Taught by

Sung Kim

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