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Neural Networks and Convolutional Neural Networks Essential Training

Offered By: LinkedIn Learning

Tags

Convolutional Neural Networks (CNN) Courses Machine Learning Courses Neural Networks Courses Keras Courses Gradient Descent Courses Activation Functions Courses Backpropagation Courses Hyperparameters Courses

Course Description

Overview

Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning.

Syllabus

Introduction
  • Welcome
  • What you should know
  • Using the exercise files
1. Introduction to Neural Networks
  • Neurons and artificial neurons
  • Gradient descent
  • The XOR challenge and solution
  • Neural networks
2. Components of Neural Networks
  • Activation functions
  • Backpropagation and hyperparameters
  • Neural network visualization
3. Neural Network Implementation in Keras
  • Understanding the components in Keras
  • Setting up a Microsoft account on Azure
  • Introduction to MNIST
  • Preprocessing the training data
  • Preprocessing the test data
  • Building the Keras model
  • Compiling the neural network model
  • Training the neural network model
  • Accuracy and evaluation of the neural network model
4. Convolutional Neural Networks
  • Convolutions
  • Zero padding and pooling
5. Convolutional Neural Networks in Keras
  • Preprocessing and loading of data
  • Creating and compiling the model
  • Training and evaluating the model
6. Enhancements to Convolutional Neural Networks (CNNs)
  • Enhancements to CNNs
  • Image augmentation in Keras
7. ImageNet
  • ImageNet challenge
  • Working with VGG16
Conclusion
  • Next steps

Taught by

Jonathan Fernandes

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