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Deep Learning Lab

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Module overview
1.1 Where Machine Learning Runs Out of Road
1.2 The Learning Mechanism, Traced By Hand
1.3 Your First PyTorch Network

Module overview
2.1 Tensors and the Shape Contract
2.2 Dataset and DataLoader: Feeding the Model
2.3 Building and Training an MLP You Can Defend
2.4 From Notebook to Project

Module overview
3.1 Optimizers and the Learning Rate
3.2 Overfitting, and What to Do About It
3.3 When Gradients Misbehave
3.4 The Diagnosis Clinic

Module overview
4.1 Data You Can Trust
4.2 Experiments You Can Reproduce
4.3 Memory, Batch Size and Precision
4.4 The Bake-Off: Gradient Boosting against a Neural Network

Module overview
5.1 Understanding Representation Learning
5.2 How Neural Networks Learn from Images
5.3 How Neural Networks Learn from Text
5.4 From Sequences to Attention
5.5 Connecting Image and Text Representations
5.6 Synthesis: Putting Representation Learning into Practice

Module overview
6.1 Understanding the Transformer Architecture
6.2 Preparing Inputs for a Transformer
6.3 Understanding Attention
6.4 The Transformer Block
6.5 Building a Decoder-Only Transformer
6.6 How Transformers Learn and Generate Text

Module overview
7.1 Understanding Pretrained Models
7.2 The Hugging Face Ecosystem (Essentials)
7.3 Finding and Selecting the Right Model
7.4 Loading and Using Pretrained Models
7.5 Building Applications with Pretrained Models
7.6 Inference and Deployment Constraints
7.7 Practical Model Selection and Integration

Module overview
8.1 Understanding Model Adaptation
8.2 Preparing Data for Fine-Tuning
8.3 Establishing Baselines
8.4 The Fine-Tuning Workflow
8.5 Parameter-Efficient Fine-Tuning (PEFT)
8.6 Fine-Tuning Large Language Models (Conceptual)
8.7 Evaluating Adapted Models

9 Evaluation, Inference & Model Economics

10 Keras 3 / TensorFlow & Model Engineering

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Deep Learning Lab

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