Basic familiarity with Python programming and machine learning

Course Outline:

Day 1: Getting Grounded in Deep Learning

1. The Core Idea

• How data becomes intelligence
• What is a neural network?
• What “deep” means
• Typical applications of deep learning: vision, text, speech

2. The Building Blocks of a Neural Network

• Neural networks: layers, neurons, and activations
• How neural networks learn patterns
• Forward pass and loss
• Backpropagation
• Parameters, weights, and biases

3. Training and Evaluating Models

• The training loop explained step-by-step
• Common problems: overfitting, underfitting, bias, variance
• Practical tricks: normalization, dropout, data augmentation
• How to interpret results (loss curves, accuracy metrics)

4. Hands-On: Building a Neural Network

• Using TensorFlow to build a neural network to classify images
• Visualizing model performance and debugging learning issues

Day 2: Using Deep Learning

1. Seeing and Understanding (Computer Vision)

• Convolutional Neural Networks (CNN)
• Why convolution is powerful for images
• CNNs as feature extractors: edges to objects
• Filters, feature maps, and pooling

2. Learning from Sequences (Language and Time Series)

• Why sequences matter
• RNNs, LSTMs and remembering context
• Time series data

3. Practical NLP

• Tokenization and embeddings
• Vocabulary
• Practical application (e.g., sentiment analysis, classification)

4. Hands-On: Applied Deep Learning

• Build, train, and use an image recognition model
• Evaluate the model’s performance

Day 3: Generative AI

1. Text Generation and Large Language Models (LLMs)

• Attention and Transformers explained
• How transformer (e.g. BERT, GPT) create coherent text
• Prompting, retrieval-augmented generation, and fine-tuning
• Practical use: summarization, code generation, chat systems
• Bias, and hallucination issues

2. 2. Image Generation and Diffusion Models

• Diffusion models (e.g. Stable Diffusion, DALLE)
• Text-to-image architecture
• Hands-on: generating and editing images using open-source models

3. Beyond Text and Images: Multimodal Generation

• Combining text, vision, and sound
• The rise of video generation and voice synthesis (e.g., Sora, Veo)
• Responsible AI and ethical considerations

4. Hands-On: Build Generative AI App

• Build a text-to-image interface or a chatbot using an open-source LLM
• Discussion of results

Training material provided: Yes (Digital format)