Neural Networks and Convolutional Neural Networks Essential Training

Neural Networks and Convolutional Neural Networks Essential Training
Neural Networks and Convolutional Neural Networks Essential Training

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 19m | 173 MB
eLearning | Skill level: Intermediate


Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this hands-on course, instructor Jonathan Fernandes covers fundamental neural and convolutional neural network concepts. Jonathan begins by providing an introduction to the components of neural networks, discussing activation functions and backpropagation. He then looks at convolutional neural networks, explaining why they’re particularly good at image recognition tasks. He also steps through how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.

Topics include:

  • Neurons and artificial neurons
  • Components of neural networks
  • Neural network visualization
  • Neural network implementation in Keras
  • Compiling and training a neural network model
  • Accuracy and evaluation of a neural network model
  • Convolutional neural networks in Keras
  • Enhancements to convolutional neural networks
  • Working with VGG16
+ Table of Contents

Introduction
1 Welcome
2 What you should know
3 Using the exercise files

Introduction to Neural Networks
4 Neurons and artificial neurons
5 Gradient descent
6 The XOR challenge and solution
7 Neural networks

Components of Neural Networks
8 Activation functions
9 Backpropagation and hyperparameters
10 Neural network visualization

Neural Network Implementation in Keras
11 Understanding the components in Keras
12 Setting up a Microsoft account on Azure
13 Introduction to MNIST
14 Preprocessing the training data
15 Preprocessing the test data
16 Building the Keras model
17 Compiling the neural network model
18 Training the neural network model
19 Accuracy and evaluation of the neural network model

Convolutional Neural Networks
20 Convolutions
21 Zero padding and pooling

Convolutional Neural Networks in Keras
22 Preprocessing and loading of data
23 Creating and compiling the model
24 Training and evaluating the model

Enhancements to Convolutional Neural Networks CNNs
25 Enhancements to CNNs
26 Image augmentation in Keras

ImageNet
27 ImageNet challenge
28 Working with VGG16

Conclusion
29 Next steps