English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 27m | 417 MB
This video will serve as an introduction to PyTorch, a dynamic, deep learning framework in Python. In this video, you will learn to create simple neural networks, which are the backbone of artificial intelligence. We will start with fundamental concepts of deep learning (including feed forward networks, back-propagation, loss functions, etc.) and then dive into using PyTorch tensors to easily create our networks. Finally, we will CUDA render our code in order to be GPU-compatible for even faster model training.
What you’ll learn—and how you can apply it
- Deep learning basics and you can apply it to your domain (X + AI)
- PyTorch platform basics and you can apply it to any deep learning problem
- CUDA rendering, which will allow you to train your networks very quickly
Table of Contents
01 Introduction to PyTorch
02 Introduction to Deep Learning
03 What is PyTorch
04 PyTorch Operations
05 Setting up a Classification Problem
06 Data Representation and Structure – Math
07 Data Representation and Structure – Code
08 Math behind Feed Forward Networks
09 Training a Neural Network for Classification – Softmax
10 Training a Neural Network for Classification – Cross-Entropy
11 Training a Neural Network for Classification – Back-Propagation
12 Creating Custom PyTorch Components
13 Proper Training Procedure for Neural Networks
14 PyTorch Basics Wrap Up
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