
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
5 Days
Basic Computer Skills and some programming experience would be beneficial
- 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
- Requirements
- Tools Needed
- Overview of the Steps
- Demonstration Building a Neural Network
- Why TensorFlow
- Computational Graph
- Regression Example
- Tensor Board
- Modularity
- Hands-On: Learning About TensorFlow
- Basics of Neural
- Networks (NN)
- Standardization
- Regularization
- Working Example
- Application Areas
- Hands-On: Using an MLP
- Understanding CNNs
- Comparison to MLP
- Using Multiple Filters
- Working Example
- Application Areas
- Hands-On: Using a CNN
- Understanding RNN
- Comparison to MLP
- LSTM
- Working Example
- Application Areas
- Hands-On: Using an LSTM Model
- Understanding Recursion
- Understanding Recursive Neural Networks
- Working Example
- Application Areas
- Hands-On: Using a Recursive Neural Network