**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

Resolve the captcha to access the links!