Adding the Dropout, Linear Layer, and ReLU to Our Model

AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker

Section 0: Introduction

Section 1: Introduction to AWS, Environment Setup, and Best Practices

Setting Up Our AWS Account (4:31)
Set Up IAM Roles + Best Practices (7:39)
AWS Security Best Practices (7:01)
Set Up AWS SageMaker Domain (2:22)
UI Domain Change (0:42)
Setting Up SageMaker Environment (5:08)
SageMaker Studio and Pricing (8:44)
Let's Have Some Fun (+ More Resources)

Section 2: Possible Resource Limit Errors Before Training and Deployment

Section 3: A Gentle Introduction to HuggingFace in SageMaker

Section 4: Gathering a Dataset for Our Multiclass Text Classification Project

Section 5: Exploratory Data Analysis

Section 6: Setting Up Our Training Notebook

Section 7: Introduction to Tokenizations and Encodings

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

Section 9: Choose Your Path

Section 10: Mathematics Behind Large Language Models and Transformers

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

Section 12: Creating the Accuracy, Training, and Validation Function

Section 13: Optimizer Functions, Model Parameters, Cross Entropy Loss Function

Section 14: Starting Our Training Job and Monitoring it in AWS CloudWatch

Section 15: Deploying our Multiclass Text Classification Endpoint in SageMaker

Section 16: Load Testing Our Machine Learning Model

Introduction to Endpoint Load Testing (4:19)
Information about the next Lesson
Creating Our Test Data for Load Testing (10:02)
Upload Testing Data to S3 (1:03)
Creating Our Model for Load Testing (3:58)
Starting Our Load Test Job (7:14)
Analyze Load Test Results (10:16)

Section 17: Production Grade Deployment of Our Machine Learning Model

Section 18: Cleaning Up Resources

Where To Go From Here?

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