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ZeroToMastery | Learn Hugging Face By Building A Custom AI Model

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Visit >>> https://onehack.us/ https://i.ibb.co/ZfnR3RG/Blue-32.png ZeroToMastery - Learn Hugging Face by Building a Custom AI Model Learn the Hugging Face ecosystem from scratch including Transformers, Datasets, Hub/Spaces, and more by building and customizing your own AI text classification model and launch it for use in the real-world! Course details: Gain real-world experience by learning how to utilize Hugging Face to solve practical problems with your own AI text classification model! What you'll learn: - How to prepare and process datasets using Hugging Face Datasets - Techniques for training and fine-tuning text classification models with Hugging Face Transformers - Methods for evaluating model performance using Hugging Face Evaluate - Steps to deploy your trained model to the Hugging Face Hub - How to create interactive demos for machine learning models using Gradio - Practical experience in the full lifecycle of a machine learning project, from data preparation to deployment Why Is This Hugging Face Project Awesome? Because it'll take your AI and machine learning skills to the next level! With this hands-on course you'll get to work directly with real-world data, building and training your own model to categorize text with accuracy. The course guides you through every step, from preparing your dataset to creating an interactive demo using Gradio, which you can proudly showcase on your Hugging Face profile. By the end, you'll have not just theoretical knowledge, but a practical, deployable model that demonstrates your ability to tackle one of the most relevant challenges in AI today. Nothing is better than seeing your work in action (well...maybe other than showing it off to potential employers)! Wait... What's a Project? One of the most common things we hear from students is: "I want to build more projects!" We love hearing that, because building projects is really the best way to learn. And unique, challenging projects can really make your portfolio stand out for potential employers. But also...it just feel so good when you actually build something real! That's why we've created ZTM Projects. A collection of comprehensive portfolio and practice projects that you can use to advance your knowledge, learn new skills, build your portfolio, and sometimes even just have fun! Prerequisites: - Basic knowledge of Python - Machine Learning knowledge recommended but not required General Details: Author: Daniel Bourke Duration: 6h 33m Released: 9/2024 Language: English MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Category: Books
Size: 1.9 GB
Added: April 23, 2026, 3:52 a.m.
Peers: Seeders: 14, Leechers: 19 (Last updated: 1 week, 1 day ago)
Files:
  1. ['01. Introduction (Hugging Face Ecosystem and Text Classification) - Zero - 1920x1080 619K.mp4'] 0 bytes
  2. ['02. More Text Classification Examples - Zero - 1920x1080 490K.mp4'] 0 bytes
  3. ["03. What We're Going To Build! - Zero - 1920x1080 810K.mp4"] 0 bytes
  4. ['04. Getting Setup Adding Hugging Face Tokens to Google Colab - Zero - 1920x1080 868K.mp4'] 0 bytes
  5. ['05. Getting Setup Importing Necessary Libraries to Google Colab - Zero - 1920x1080 914K.mp4'] 0 bytes
  6. ['06. Downloading a Text Classification Dataset from Hugging Face Datasets - Zero - 1920x1080 818K.mp4'] 0 bytes
  7. ['07. Preparing Text Data for Use with a Model - Part 1 Turning Our Labels into Numbers - Zero - 1920x1080 661K.mp4'] 0 bytes
  8. ['08. Preparing Text Data for Use with a Model - Part 2 Creating Train and Test Sets - Zero - 1920x1080 582K.mp4'] 0 bytes
  9. ['09. Preparing Text Data for Use with a Model - Part 3 Getting a Tokenizer - Zero - 1920x1080 892K.mp4'] 0 bytes
  10. ['10. Preparing Text Data for Use with a Model - Part 4 Exploring Our Tokenizer - Zero - 1920x1080 700K.mp4'] 0 bytes
  11. ['11. Preparing Text Data for Use with a Model - Part 5 Creating a Function to Tokenize Our Data - Zero - 1920x1080 883K.mp4'] 0 bytes
  12. ['12. Setting Up an Evaluation Metric (to measure how well our model performs) - Zero - 1920x1080 788K.mp4'] 0 bytes
  13. ['13. Introduction to Transfer Learning (a powerful technique to get good results quickly) - Zero - 1920x1080 780K.mp4'] 0 bytes
  14. ['14. Model Training - Part 1 Setting Up a Pretrained Model from the Hugging Face Hub - Zero - 1920x1080 1022K.mp4'] 0 bytes
  15. ['15. Model Training - Part 2 Counting the Parameters in Our Model - Zero - 1920x1080 903K.mp4'] 0 bytes
  16. ['16. Model Training - Part 3 Creating a Folder to Save Our Model - Zero - 1920x1080 856K.mp4'] 0 bytes
  17. ['17. Model Training - Part 4 Setting Up Our Training Arguments with TrainingArguments - Zero - 1920x1080 1233K.mp4'] 0 bytes
  18. ['18. Model Training - Part 5 Setting Up an Instance of Trainer with Hugging Face Transformers - Zero - 1920x1080 1119K.mp4'] 0 bytes
  19. ['19. Model Training - Part 6 Training Our Model and Fixing Errors Along the Way - Zero - 1920x1080 933K.mp4'] 0 bytes
  20. ['20. Model Training - Part 7 Inspecting Our Models Loss Curves - Zero - 1920x1080 807K.mp4'] 0 bytes
  21. ['21. Model Training - Part 8 Uploading Our Model to the Hugging Face Hub - Zero - 1920x1080 1009K.mp4'] 0 bytes
  22. ['22. Making Predictions on the Test Data with Our Trained Model - Zero - 1920x1080 865K.mp4'] 0 bytes
  23. ['23. Turning Our Predictions into Prediction Probabilities with PyTorch - Zero - 1920x1080 839K.mp4'] 0 bytes
  24. ["24. Sorting Our Model's Predictions by Their Probability - Zero - 1920x1080 651K.mp4"] 0 bytes
  25. ['25. Performing Inference - Part 1 Discussing Our Options - Zero - 1920x1080 807K.mp4'] 0 bytes
  26. ['26. Performing Inference - Part 2 Using a Transformers Pipeline (one sample at a time) - Zero - 1920x1080 890K.mp4'] 0 bytes
  27. ['27. Performing Inference - Part 3 Using a Transformers Pipeline on Multiple Samples at a Time (Batching) - Zero - 1920x1080 1160K.mp4'] 0 bytes
  28. ['28. Performing Inference - Part 4 Running Speed Tests to Compare One at a Time vs. Batched Predictions - Zero - 1920x1080 826K.mp4'] 0 bytes
  29. ['29. Performing Inference - Part 5 Performing Inference with PyTorch - Zero - 1920x1080 851K.mp4'] 0 bytes
  30. ['30. OPTIONAL - Putting It All Together from Data Loading, to Model Training, to making Predictions on Custom Data - Zero - 1920x1080 848K.mp4'] 0 bytes
  31. ['31. Turning Our Model into a Demo - Part 1 Gradio Overview - Zero - 1920x1080 997K.mp4'] 0 bytes
  32. ['32. Turning Our Model into a Demo - Part 2 Building a Function to Map Inputs to Outputs - Zero - 1920x1080 684K.mp4'] 0 bytes
  33. ['33. Turning Our Model into a Demo - Part 3 Getting Our Gradio Demo Running Locally - Zero - 1920x1080 777K.mp4'] 0 bytes
  34. ['34. Making Our Demo Publicly Accessible - Part 1 Introduction to Hugging Face Spaces and Creating a Demos Directory - Zero - 1920x1080 650K.mp4'] 0 bytes
  35. ['35. Making Our Demo Publicly Accessible - Part 2 Creating an App File - Zero - 1920x1080 980K.mp4'] 0 bytes
  36. ['36. Making Our Demo Publicly Accessible - Part 3 Creating a README File - Zero - 1920x1080 820K.mp4'] 0 bytes
  37. ['37. Making Our Demo Publicly Accessible - Part 4 Making a Requirements File - Zero - 1920x1080 872K.mp4'] 0 bytes
  38. ['38. Making Our Demo Publicly Accessible - Part 5 Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available - Zero - 1920x1080 940K.mp4'] 0 bytes
  39. ['39. Summary Exercises and Extensions - Zero - 1920x1080 1213K.mp4'] 0 bytes
  40. ['OneHack.Us - Free Tutorials, Guides, Courses, Community Forum & more!.txt'] 0 bytes
  41. ['Onehack.us - Together we learn!.url'] 0 bytes

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