GitHub - practicalAI/practicalAI: 📚 A practical approach to machine learning.

README.md

Notebooks

• 🌎 → https://practicalai.me
• All of these notebooks are in TensorFlow 2.0 + Keras but you can find old PyTorch notebooks in the v0.1 release.
• 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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