/practicalAI

📚 A practical approach to machine learning.

Primary LanguageJupyter NotebookMIT LicenseMIT

A practical approach to machine learning.

Created by Goku Mohandas and contributors

Notebooks

  • 📚 Illustrative ML notebooks in TensorFlow 2.0 + Keras.
  • ⚒️ Build robust models using the functional API w/ custom components
  • 📦 Train using simple yet highly customizable loops to build products fast
  • If you prefer Jupyter Notebooks or want to add/fix content, check out the notebooks directory.

Basic ML
Basics Machine Learning Tools Deep Learning
  • Learn Python basics with notebooks.
  • Use data science libraries like NumPy and Pandas.
  • Implement basic ML models in TensorFlow 2.0 + Keras.
  • Create deep learning models for improved performance.
 📓 Notebooks 📈 Linear Regression 🔎 Data & Models ️🖼 Convolutional Neural Networks
🐍 Python 📊 Logistic Regression 🛠 Utilities 👑 Embeddings
🔢 NumPy ️🎛 Multilayer Perceptrons ️✂️ Preprocessing 📗 Recurrent Neural Networks
🐼 Pandas

Production ML
Local Applications Scale Miscellaneous
  • Setup your local environment for ML.
  • Wrap your ML in RESTful APIs using Flask to create applications.
  • Standardize and scale your ML applications with Docker and Kubernetes.
  • Deploy simple and scalable ML workflows using Kubeflow.
 💻 Local Setup 🌲 Logging 🐳 Docker 🤝 Distributed Training
🐍 ML Scripts ⚱️ Flask Applications 🚢 Kubernetes 🔋 Databases
✅ Unit Tests 🌊 Kubeflow 🔐 Authentication

Advanced ML
General Sequential Popular Miscellaneous
  • Dive into architectural and interpretable advancements in neural networks.
  • Implement state-of-the-art NLP techniques.
  • Learn about popular deep learning algorithms used for generation, time-series, etc.
 🧐 Attention 🐝 Transformers 🎭 Generative Adversarial Networks 🔮 Autoencoders
🏎️ Highway Networks 👹 BERT, GPT2, XLNet 🎱 Bayesian Deep Learning 🕷️ Graph Neural Networks
💧 Residual Networks 🕘 Temporal CNNs 🍒 Reinforcement Learning

Topics
Computer Vision Natural Language Unsupervised Learning Miscellaneous
  • Learn how to use deep learning for computer vision tasks.
  • Implement techniques for natural language tasks.
  • Derive insights from unlabeled data using unsupervised learning.
 📸 Image Recognition 📖 Text classification 🍡 Clustering ⏰ Time-series Analysis
🖼️ Image Segmentation 💬 Named Entity Recognition 🏘️ Topic Modeling 🛒 Recommendation Systems
🎨 Image Generation 🧠 Knowledge Graphs 🎯 One-shot Learning
🗃️ Interpretability

Updates

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