Neural Networks and Convolutional Neural Networks Essential Training (2018)
1h 19mIntermediate2018-05-04
Authors
Jonathan Fernandes
Consultant focusing on data science, AI, and big data
Course details
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.
Learning objectives
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
Learning objectives
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
Skills covered
KerasNeural Networks and Deep LearningPythonEssential TrainingArtificial Intelligence (AI)Open Source
Concepts
0. Introduction
- 01 - Welcome
- 02 - What you should know
- 03 - Using the exercise files
1. Introduction to Neural Networks
- 04 - Neurons and artificial neurons
- 05 - Gradient descent
- 06 - The XOR challenge and solution
- 07 - Neural networks
2. Components of Neural Networks
- 08 - Activation functions
- 09 - Backpropagation and hyperparameters
- 10 - Neural network visualization
3. 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
4. Convolutional Neural Networks
- 20 - Convolutions
- 21 - Zero padding and pooling
5. Convolutional Neural Networks in Keras
- 22 - Preprocessing and loading of data
- 23 - Creating and compiling the model
- 24 - Training and evaluating the model
6. Enhancements to Convolutional Neural Networks (CNNs)
- 25 - Enhancements to CNNs
- 26 - Image augmentation in Keras
7. ImageNet
- 27 - ImageNet challenge
- 28 - Working with VGG16
Conclusion
- 29 - Next steps