Overview of Neural Networks and Deep Learning
This course explains Neural Networks (NN) and Deep Learning (DL). At the end of the course, participant will understand the specific use cases and the NN/DL models developed for these areas
Learning Objectives
In this course, participants will:
- Understand the background of Neural Networks and Deep Learning
- Know how to use a neural network and Understand the data needs of deep learning
- Have a working knowledge of MLP
- Work with class to word with examples of each model and as well explore the hyperparameters for each model.
Duration
5 Days
Prerequisite for Neural Networks and Deep Learning
Basic Computer Skills and some programming experience would be beneficial.
Course Outline for Neural Networks and Deep Learning
Unit 1: Overview of Neural Networks (NN) and Deep Learning(DL)
- What is Machine Learning?
- What is a Neural Network?
- What is Deep Learning?
- What is Artificial Intelligence?
- What Can Be Learned from Deep Learning?
- Deep DL vs AI
Unit 2: Building a Neural Network (NN)
- Requirements
- Tools Needed
- Overview of the Steps
- Demonstration Building a Neural Network
Unit 3: TensorFlow
- Why TensorFlow
- Computational Graph
- Regression Example
- Tensor Board
- Modularity
- Hands-On: Learning About TensorFlow
Unit 4: MLP Networks
- Basics of Neural
- Networks (NN)
- Standardization
- Regularization
- Working Example
- Application Areas
- Hands-On: Using an MLP
Unit 5: Convolutional Neural Networks (CNN)
- Understanding CNNs
- Comparison to MLP
- Using Multiple Filters
- Working Example
- Application Areas
- Hands-On: Using a CNN
Unit 6: Recurrent Neural Networks (RNN)
- Understanding RNN
- Comparison to MLP
- LSTM
- Working Example
- Application Areas
- Hands-On: Using an LSTM Model
Unit 7: Recursive Neural Networks
- Understanding Recursion
- Understanding Recursive Neural Networks
- Working Example
- Application Areas
- Hands-On: Using a Recursive Neural Network