Example Curriculum
Introduction
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Course Overview
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Project 0 - Introduction to Text Classification
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Project 0 - Let's Get Started!
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Project 0 - Preparing Text Data & Evaluation Metric
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- Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers (12:48)
- Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets (6:18)
- Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer (12:53)
- Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer (10:26)
- Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data (17:57)
- Setting Up an Evaluation Metric (to measure how well our model performs) (8:53)
- Let's Have Some Fun (+ More Resources)
Project 0 - Model Training
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- Introduction to Transfer Learning (a powerful technique to get good results quickly) (7:10)
- Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub (12:19)
- Model Training - Part 2: Counting the Parameters in Our Model (12:27)
- Model Training - Part 3: Creating a Folder to Save Our Model (3:53)
- Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments (14:59)
- Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers (5:05)
- Model Training - Part 6: Training Our Model and Fixing Errors Along the Way (13:34)
- Model Training - Part 7: Inspecting Our Models Loss Curves (14:39)
- Model Training - Part 8: Uploading Our Model to the Hugging Face Hub (8:01)
- Unlimited Updates
Project 0 - Making Predictions
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Project 0 - Performing Inference
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- Performing Inference - Part 1: Discussing Our Options (9:40)
- Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time) (10:01)
- Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching) (6:38)
- Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions (10:33)
- Performing Inference - Part 5: Performing Inference with PyTorch (12:06)
- OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data (34:28)
- Implement a New Life System
Project 0 - Launching Our Model!
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- Turning Our Model into a Demo - Part 1: Gradio Overview (3:47)
- Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs (7:07)
- Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally (6:46)
- Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory (8:01)
- Making Our Demo Publicly Accessible - Part 2: Creating an App File (12:14)
- Making Our Demo Publicly Accessible - Part 3: Creating a README File (7:07)
- Making Our Demo Publicly Accessible - Part 4: Making a Requirements File (3:33)
- Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available (18:43)
- Summary Exercises and Extensions (5:55)
- Course Check-In
Project 1 - Building a Custom Object Detection Model with Hugging Face Transformers
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- Project 1 Overview and Resources
- Introduction (10:03)
- Setting Up Google Colab with Hugging Face Tokens (5:51)
- Installing Necessary Dependencies (3:43)
- Getting an Object Detection Dataset (7:37)
- Inspecting the Features of Our Dataset (6:23)
- Creating a Colour Palette to Visualize Our Classes (9:35)
- Creating a Helper Function to Halve Our Image Sizes (4:24)
- Creating a Helper Function to Halve Our Box Sizes (6:01)
- Testing our Helper Functions (4:33)
- Outlining the Steps to Draw Boxes on an Image (6:26)
- Plotting Bounding Boxes on a Single Image Step by Step (19:04)
- Different Bounding Box Formats (8:17)
Project 1 - Getting and Object Detection Model and Image Preprocessor
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- Getting an Object Detection Model (6:15)
- Transfer Learning Overview (6:08)
- Downloading our Model from the Hugging Face Hub and Trying it Out (9:26)
- Inspecting the Layers of Our Model (6:53)
- Counting the Number of Parameters in Our Model (10:54)
- Creating a Function to Build Our Custom Model (13:15)
- Passing a Single Image Sample Through Our Model - Part 1 (15:47)
- OPTIONAL: Data Preprocessor Model Workflow (8:46)
- Loading Our Models Image Preprocessor and Customizing it for Our Use Case (20:10)
- Exercise: Imposter Syndrome (2:55)
Project 1 - Getting Hands-on with Different Bounding Box Formats
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- Bounding Box Formats 101
- Discussing the Format Our Model Expects Our Annotations In (COCO) (6:17)
- Creating Dataclasses to Hold the COCO Format (9:54)
- Creating a Function to Turn Our Annotations into COCO Format (12:05)
- Preprocessing a Single Image Sample and COCO Formatted Annotations (7:26)
- Post Processing a Single Output (12:02)
- Plotting a Single Post Processed Sample onto an Image (12:44)
Project 1 - OPTIONAL: Reproducing our Model's Post Processed Outputs by Hand
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- OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 1: Overview (10:44)
- OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 2: Replicating Scores by Hand (28:32)
- OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 3: Replicating Labels by Hand (12:32)
- OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 4: Replicating Boxes by Hand Overview (10:23)
- OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 5: Replicating Boxes by Hand Implementation (17:41)
- OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 6: Plotting Our Manual Post Processed Outputs on an Image (6:44)
Project 1 - Preparing our Data for Training our Object Detection Model
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- Preparing Our Data at Scale - Part 1: Concept Overview (9:21)
- Preparing Our Data at Scale - Part 2: Creating Train Validation and Test Splits (12:13)
- Preparing Our Data at Scale - Part 3: Preprocessing Multiple Samples at a Time Overview (8:16)
- Preparing our Data at Scale - Part 4: Making a Function to Preprocess Multiple Samples at a Time (21:37)
- Preparing our Data at Scale - Part 5: Applying Our Preprocessing Function to Our Datasets (9:37)
- Preparing Our Data at Scale - Part 6: Creating a Data Collation Function (12:20)
Project 1 - Training a Custom Object Detection Model for Trashify
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- Training a Custom Model - Part 1: Overview (7:42)
- Training a Custom Model - Part 2: Creating a Model and Folder to Save Our Model to (4:11)
- Training a Custom Model - Part 3: Creating TrainingArguments for Our Model Overview (12:53)
- Training a Custom Model - Part 4: Creating our First TrainingArguments (11:11)
- Training a Custom Model - Part 5: Finishing Off the TrainingArguments (12:39)
- Training a Custom Model - Part 6: OPTIONAL - Creating a Custom Optimizer for Different Learning Rates (16:05)
- Training a Custom Model - Part 7: Creating an Evaluation Function for Our Model Overview (13:09)
- Training a Custom Model - Part 8: Creating an Evaluation Function for Our Model Targets Processing (22:49)
- Training a Custom Model - Part 9: Creating an Evaluation Function for Our Model Predictions Processing (13:52)
- Training a Custom Model - Part 10: Training Our Model with Trainer (12:53)
- Training a Custom Model - Part 11: Plotting Our Models Loss Curves (8:35)
Project 1 - Evaluating our Trained Object Detection Model
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Project 1 - Bringing Trashify to Life: Turning our Custom Model into a Shareable Demo
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- Turning Our Model into a Demo - Part 1: Gradio and Hugging Face Spaces Overview (10:10)
- Turning Our Model into a Demo - Part 2: Creating an App File Overview (7:10)
- Turning Our Model into a Demo - Part 3: Building the Main Function of Our App File (27:32)
- Turning Our Model into a Demo - Part 4: Finishing Off Our App File and Testing Our Demo (9:56)
- Turning Our Model into a Demo - Part 5: Creating a Readme and Requirements File (3:31)
- Turning Our Model into a Demo - Part 6: Getting Example Images for Our Demo (8:19)
- Turning Our Model into a Demo - Part 7: Uploading Our Demo to the Hugging Face Hub (17:18)
- Turning Our Model into a Demo - Part 8: Embedding Our Demo into Our Notebook (3:44)
- Summary, Extensions and Extra-Curriculum (6:15)
Project 2 - Fully Fine-tuning a Small Language Model (SLM) with Hugging Face Transformers
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Project 2 - Setting up a Small Language Model and Getting a Dataset
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Project 2 - Preparing a text dataset for language model fine-tuning
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Project 2 - Testing our Small Language Model (before fine-tuning)
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Project 2 - Fine-Tuning Our Model On Custom Data
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- Fine-Tuning Our Model - Part 1: Discussing the Ingredients We Need for Fine-Tuning (5:09)
- Fine-Tuning Our Model - Part 2: Setting Up Our SFTConfig with Hyperparameters for Model Training (17:53)
- Fine-Tuning Our Model - Part 3: Setting Up Our SFTTrainer and Fine-Tuning Our Model and Fixing Errors Along the Way (15:21)
- Fine-Tuning Our Model - Part 4: Plotting Our Models Loss Curves (5:54)
- Fine-Tuning Our Model - Part 5: Saving Our Best Model to File (4:42)
Project 2 - Loading Our Fine-Tuned Model and Making Predictions
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Project 2 - Evaluating Our Fine-Tuned Small Language Model
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Project 2 - Uploading Our Model to the Hugging Face Hub
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Project 2 - Turning Our Fine-Tuned Model Into a Shareable Demo
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- Turning Our Model into a Demo - Part 1: Discussing the Required Components (6:13)
- Turning Our Model into a Demo - Part 2: Building an app.py File to Launch Our Demo (18:09)
- Turning Our Model into a Demo - Part 3: Creating Requirements and README Files for Our Demo (4:11)
- Turning Our Model into a Demo - Part 4: Uploading Our Demo to the Hugging Face Hub and Publishing it (13:05)
- Turning Our Model into a Demo - Part 5: Embedding Our Demo Right within Google Colab (4:31)
Where To Go From Here?
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