Video description
In this video course, you will learn the basic principles of neural networks that are used to build models. You'll start by seeing machine learning, neurons, activations, activation functions, weights, and how everything works under the hood. Next, you'll cover the basics of the learning loop including how backpropagation and gradient descent work. Further, you will learn about convolutions, how they are inspired by the animal visual cortex, and how we use them in neural networks. One of the focuses of the course is image classification and detecting common objects in images. This has many uses in your day-to-day projects. We will be using the PyTorch open-source neural network library here.
The course will also cover current state-of-the-art neural network models and show how to use them even on smaller hardware. The video concludes by showing some common tricks with hyperparameter settings and regularization techniques, and how to use neural networks in production environments.
What You Will Learn
- Discover the basics of neural networks and how they function
- Work with convolutional neural networks
- Use CNNs in your day-to-day work for image classification and other tasks
Who This Video Is For
Data scientists and machine learning and deep learning engineers.
Table of Contents
Introduction to Convolutional Neural Networks and Image Classification
00:03:51
Introduction to Artificial Neural Networks
Universal Function Approximators
00:03:05
What are Artificial Neural Networks? Part 1
00:02:55
What are Artificial Neural Networks? Part 2
00:04:28
Hyperparameters
00:04:43
Let’s Build a Neural Network!
00:05:07
Example 1 – Imports
00:03:17
Example 1 – Model Definition
00:06:21
Example 1 – Data Loading
00:02:45
Example 1 – Training Loop
00:05:11
Example 1 – Testing Loop
00:03:22
Example 1 – Testing our model!
00:05:49
Convolutional Neural Networks
Convolutions and Convolutional Neural Networks
00:08:49
Example 2 – Convolutional Model
00:08:10
Pretrained Models
00:03:34
Example 3 – Pretrained models
00:05:27
Exposing Neural Network Models
Exposing Neural Network Models
00:01:18
Example 4 – Exposing Models
00:02:42