Example Curriculum
Section 0: Introduction
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Section 1: Introduction to AWS, Environment Setup, and Best Practices
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Section 2: Possible Resource Limit Errors Before Training and Deployment
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Section 3: A Gentle Introduction to HuggingFace in SageMaker
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Section 4: Gathering a Dataset for Our Multiclass Text Classification Project
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Section 5: Exploratory Data Analysis
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Section 6: Setting Up Our Training Notebook
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Section 7: Introduction to Tokenizations and Encodings
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- Creating Our Optional Experiment Notebook - Part 1 (3:21)
- Creating Our Optional Experiment Notebook - Part 2 (4:01)
- Encoding Categorical Labels to Numeric Values (13:24)
- Understanding the Tokenization Vocabulary (15:05)
- Encoding Tokens (10:56)
- Practical Example of Tokenization and Encoding (12:48)
- Course Check-In
Section 8: Setting Up Data Loading with PyTorch
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Section 9: Choose Your Path
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Section 10: Mathematics Behind Large Language Models and Transformers
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- DistilBert vs. Bert Differences (4:46)
- Embeddings In A Continuous Vector Space (7:40)
- Introduction To Positional Encodings (5:13)
- Positional Encodings - Part 1 (4:14)
- Positional Encodings - Part 2 (Even and Odd Indices) (10:10)
- Why Use Sine and Cosine Functions (5:08)
- Understanding the Nature of Sine and Cosine Functions (9:52)
- Visualizing Positional Encodings in Sine and Cosine Graphs (9:24)
- Solving the Equations to Get the Values for Positional Encodings (18:07)
- Introduction to Attention Mechanism (3:02)
- Query, Key and Value Matrix (18:10)
- Getting Started with Our Step by Step Attention Calculation (6:53)
- Calculating Key Vectors (20:05)
- Query Matrix Introduction (10:20)
- Calculating Raw Attention Scores (21:24)
- Understanding the Mathematics Behind Dot Products and Vector Alignment (13:32)
- Visualizing Raw Attention Scores in 2D (5:42)
- Converting Raw Attention Scores to Probability Distributions with Softmax (9:16)
- Normalization (3:19)
- Understanding the Value Matrix and Value Vector (9:07)
- Calculating the Final Context Aware Rich Representation for the Word "River" (10:45)
- Understanding the Output (1:58)
- Understanding Multi Head Attention (11:55)
- Multi Head Attention Example and Subsequent Layers (9:51)
- Masked Language Learning (2:29)
- Exercise: Imposter Syndrome (2:56)
Section 11: Customizing our Model Architecture in PyTorch
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Section 12: Creating the Accuracy, Training, and Validation Function
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Section 13: Optimizer Functions, Model Parameters, Cross Entropy Loss Function
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Section 14: Starting Our Training Job and Monitoring it in AWS CloudWatch
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Section 15: Deploying our Multiclass Text Classification Endpoint in SageMaker
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Section 16: Load Testing Our Machine Learning Model
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Section 17: Production Grade Deployment of Our Machine Learning Model
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Section 18: Cleaning Up Resources
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Where To Go From Here?
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