Deep Learning in Theory and Practice Training in Ireland

Module 1: Deep Learning Introduction

• Terminology

• AI topics and techniques

• Current use of AI

• New frontiers of AI

• Supervised learning

• Unsupervised learning

• Reinforcement learning

Module 2: Artificial Neural Network

• Artificial neuron

• Activation function

• Forward pass and backward pass

• Loss function and gradient descent

• Fully connected neural network

• Convolutional neural network

• Recurrent neural network

• Training and inference

Module 3: Deep Learning Programming Overview

• Python

• NumPy

• Pandas

• TensorFlow

• Keras

Module 4: Pre-trained Models

• Image classification

• Object detection

• Word embedding

Module 5: Regularization

• Underfitting and overfitting

• Bias-variance tradeoff

• Dataset augmentation

• Early stopping

• L1 and L2 regularization

• Dropout

• Adversarial training

• Ensemble method

Module 6: Optimization and Tuning

• Learning rate

• Momentum

• Optimization algorithm

• Parameter initialization strategy

• Data normalization

• Batch normalization

• Hyperparameter tuning strategy

• Hardware acceleration

• HPE Deep Learning Cookbook

Module 7: Deep Learning Project Management

• Deep learning project management

• Data acquisition

• Data preprocessing

• Data labelling

• Baselining

• Data augmentation

• Transfer learning

• Performance measurement

• Ensemble method

Module 8: AI in Law and Ethics

• AI in law

• All in ethics

Module 9: Capstone Project

• Apply deep learning to a real-life use case