This training covers the foundations of artificial intelligence and machine learning, with special focus on deep learning. Best practices in managing deep learning projects will be shared. Hands-on labs in Python, TensorFlow and Keras will be provided to deepen student understanding.
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
Join our public courses in our Canada facilities. Private class trainings will be organized at the location of your preference, according to your schedule.