Skip to main content

Introduction to Deep Learning Using PyTorch

Introduction to Deep Learning Using PyTorch
| Pub Date: 6th February 2018 | | Pages/Duration: 1h 27m | Language: English | Format: MP4 AVC 1280×720 AAC 44KHz 2ch | Size: 417 MB

Download


Download from Turbobit
Download from DepositFiles
Download from Rapidgator
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