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
-
Introduction to Artificial Neural Networks
- Universal Function Approximators
- What are Artificial Neural Networks? Part 1
- What are Artificial Neural Networks? Part 2
- Hyperparameters
- Let’s Build a Neural Network!
- Example 1 – Imports
- Example 1 – Model Definition
- Example 1 – Data Loading
- Example 1 – Training Loop
- Example 1 – Testing Loop
- Example 1 – Testing our model!
- Convolutional Neural Networks
- Exposing Neural Network Models
Product information
- Title: Introduction to Convolutional Neural Networks: With Image Classification Using PyTorch
- Author(s):
- Release date: January 2020
- Publisher(s): Apress
- ISBN: 9781484256480
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