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https://github.com/bradflaugher/ai-101

Notes, links and code samples and resources for teaching yourself pytorch and tensorflow.
https://github.com/bradflaugher/ai-101

bootcamp course data-engineering data-science learn-to-code learning-by-doing learning-python machine-learning

Last synced: 3 days ago
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Notes, links and code samples and resources for teaching yourself pytorch and tensorflow.

Host: GitHub
URL: https://github.com/bradflaugher/ai-101
Owner: bradflaugher
License: gpl-3.0
Created: 2022-03-31T23:23:24.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-06-21T14:08:59.000Z (5 months ago)
Last Synced: 2024-06-24T02:11:29.265Z (5 months ago)
Topics: bootcamp, course, data-engineering, data-science, learn-to-code, learning-by-doing, learning-python, machine-learning
Language: Jupyter Notebook
Homepage:
Size: 19.1 MB
Stars: 12
Watchers: 4
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # AI-101 🚀

Lecture notes, readings, code samples and resources for teaching yourself how PyTorch and Tensorflow.

This class was formerly taught as a bootcamp but is now a self-paced online resource.

# More Like This ⭐

See Brad's [GitHub Stars](https://github.com/predbrad?submit=Search&q=learn&tab=stars&type=&sort=&direction=&submit=Search) for a curated collection of helpful resources.

# Course Outline 📝

| Session   | Topic | TODO |

|-----------|---------| ---------|

| Preparation | N/A | See [preparation.md](./preparation.md) |

| 📅 Week 1 | 📚 [Roles, Machine Learning Basics, Tech Stack](./30000.md)| [How Deep Learning Works](https://www.youtube.com/watch?v=wBgW3ZtlPT8) and [Intro to Free Software](https://www.fsf.org/blogs/rms/20140407-geneva-tedx-talk-free-software-free-society) |

| 📅 Week 1 | 🎬 ["Foundations" Live Demos](./foundations.md) | [SuperDataScience Code Interpreter Guide](https://youtu.be/ac6580tuveo?si=dUlyNu1CzFkhDPek) |

| 📅 Week 2 | 💡 [Final Project Ideas](#-final-project-sequence-) | Pick a project! |

| 📅 Week 2 | 📊 [The Universal ML Workflow](https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-6) | [The Universal ML Workflow](https://towardsdatascience.com/applying-the-universal-machine-learning-workflow-to-the-uci-mushroom-dataset-1939442d44e7) and [The Regression Theory of Everything](./AI_harmony_c3.pdf) |

| 📅 Week 3 | 👥 Pair Programming, Project Q&A | work on your project |

| 📅 Week 3 | 📝 [Data Engineering, ETL Basics](./etl.md) and [Data Wrangling Techniques](./wranglin.md) | work on your project |

| 📅 Week 4 | 📊  OpenRouter [Sommelier App](https://github.com/Medusa-ML/LLMmMm) in AWS, Azure and GCP Cloud Setup Demo | Setup [AWS Free Tier](https://aws.amazon.com/free/) |

| 📅 Week 5 | 🧠 [Metrics and Loss Functions, Model Architecture and Hyperparameters](./architecture.md) | [60 Minute Pytorch Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) |

| 📅 Week 7 | 🎓 [After The Bootcamp...](./after_bootcamp.md) | go forth and win! |

| 📅 Weeks 7-8 | 🎉 [Project Presentations](https://www.youtube.com/@bradflaugher2452/videos)  | brag about yourself online |

---

# 🚀 **Final Project Sequence** 🚀

Participants will dedicate substantial time to final projects aligned with their ✨career aspirations✨. For an array of past presentations and source code, 📺 visit [Brad's Youtube Channel](https://www.youtube.com/@bradflaugher2452/videos).

| **Step**                                | **Details**                                                                                                                                                                                                                     |

|-----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|

| **🅰️ Data Collection and Simple Model** | Generate synthetic text data using [Scikit-LLM](https://github.com/iryna-kondr/scikit-llm). Explore datasets on [Kaggle](https://www.kaggle.com/datasets) or [Hugging Face](https://huggingface.co/datasets). |

| **🅱️ Custom Model (Optional)**         | **Beginner:** Follow [TensorFlow quickstart](https://www.tensorflow.org/tutorials) for basic image/text classifiers. **Recommended:** Dive into [60 Minute PyTorch Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html), then explore 📜 [PyTorch Text Classification](https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html) or [PyTorch Image Classification](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html). **More Advanced:** Fine-tune pretrained models with [Hugging Face](https://huggingface.co/docs/transformers/training) for 📝 text or 📸 image classification. **Apple Nerds Only** Use [MLX](https://github.com/ml-explore/mlx). |

| **⭐ Challenge and Experiment (Optional)**| **Advanced:** Explore cross-platform frameworks like [Keras Core](https://keras.io/keras_core/guides/). 🏆 **Very Advanced:** Attempt to [Replicate a Winning Model from Kaggle](https://www.kaggle.com/code?types=competitions).                                                                                     |

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