backwardn / practicalai

📚 A practical approach to machine learning.

A practical approach to machine learning.

_{Created by Goku Mohandas and contributors}

Notebooks

• 🌎 → https://madewithml.com
• 📚 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