anam0931/practicalAI

📚 A practical approach to machine learning.

Jupyter NotebookMIT

A practical approach to machine learning.

_{Created by
Goku Mohandas and

contributors}

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
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
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
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
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

📬 Newsletter - Subscribe to get updates on new content