Learn Hugging Face by Building a Custom AI Model

Name: Learn Hugging Face by Building a Custom AI Model
Price: 9 USD
Availability: InStock

6h 32m 55s

English

Paid

October 7, 2024

Explore the Hugging Face ecosystem from scratch, including Transformers, Datasets, Hub/Spaces, and much more, by creating and configuring your own AI model for text classification. In this course, you will learn how to deploy your model for real-world use!

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#	Title	Duration
1	Introduction (Hugging Face Ecosystem and Text Classification)	06:53
2	More Text Classification Examples	04:41
3	What We're Going To Build!	07:22
4	Getting Setup: Adding Hugging Face Tokens to Google Colab	05:53
5	Getting Setup: Importing Necessary Libraries to Google Colab	09:36
6	Downloading a Text Classification Dataset from Hugging Face Datasets	16:01
7	Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers	12:49
8	Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets	06:19
9	Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer	12:54
10	Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer	10:27
11	Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data	17:58
12	Setting Up an Evaluation Metric (to measure how well our model performs)	08:54
13	Introduction to Transfer Learning (a powerful technique to get good results quickly)	07:11
14	Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub	12:20
15	Model Training - Part 2: Counting the Parameters in Our Model	12:27
16	Model Training - Part 3: Creating a Folder to Save Our Model	03:54
17	Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments	15:00
18	Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers	05:06
19	Model Training - Part 6: Training Our Model and Fixing Errors Along the Way	13:35
20	Model Training - Part 7: Inspecting Our Models Loss Curves	14:40
21	Model Training - Part 8: Uploading Our Model to the Hugging Face Hub	08:02
22	Making Predictions on the Test Data with Our Trained Model	05:59
23	Turning Our Predictions into Prediction Probabilities with PyTorch	12:49
24	Sorting Our Model's Predictions by Their Probability	05:11
25	Performing Inference - Part 1: Discussing Our Options	09:41
26	Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time)	10:02
27	Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching)	06:39
28	Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions	10:34
29	Performing Inference - Part 5: Performing Inference with PyTorch	12:07
30	OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data	34:29
31	Turning Our Model into a Demo - Part 1: Gradio Overview	03:48
32	Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs	07:08
33	Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally	06:47
34	Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory	08:02
35	Making Our Demo Publicly Accessible - Part 2: Creating an App File	12:15
36	Making Our Demo Publicly Accessible - Part 3: Creating a README File	07:08
37	Making Our Demo Publicly Accessible - Part 4: Making a Requirements File	03:34
38	Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available	18:44
39	Summary Exercises and Extensions	05:56