Summary
PyTorch is the most popular framework for high-performance numerical computation across a wide range of CPU and GPU hardware. While originally intended for quick development of Deep Learning applications, PyTorch is capable of much more and easily applicable to a wide range of numerical applications. In this course, you learn how to leverage the ease of use and power of PyTorch for all your Machine Learning needs. We introduce classical ML and Deep Learning algorithms, analyze their pros and cons, explore the best way to evaluate their performance, and practice how to implement them using PyTorch through a number of practical examples designed to best get you up and running quickly.
Program
Machine Learning Overview
Basic Principles
Unsupervised Learning
Supervised Learning
Self-Supervised Learning
Machine Learning Pipelines
Machine Learning as Optimization
PyTorch Overview
Unsupervised Learning
Use Cases
Data Preparation
Principal Component Analysis
K-Means
DBScan
Latent Dirichlet Analysis (LDA)
Supervised Learning
Linear Regression
Logistic Regression (Classification)
K-Nearest Neighbors
Support Vector Machines
Neural Networks
Perceptrons
Activation Functions
Feed Forward Networks
Deep Learning
Deep Learning Applications
Convolutional Neural Networks
Generative Adversarial Networks
Recurrent Neural Networks
Graph Neural Networks