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๐Ÿ“š A practical approach to machine learning.
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A practical approach to machine learning.

Created by Goku Mohandas and contributors


  • ๐Ÿ“š Illustrative ML notebooks in TensorFlow 2.0 + Keras.
  • โš’๏ธ Build robust models using the functional API w/ custom components
  • ๐Ÿ“ฆ 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.
๐Ÿ““ Notebooks ๐Ÿ“ˆ Linear Regression ๐Ÿ”Ž Data & Models ๏ธ๐Ÿ–ผ Convolutional Neural Networks
๐Ÿ Python ๐Ÿ“Š Logistic Regression ๐Ÿ›  Utilities ๐Ÿ‘‘ Embeddings
๐Ÿ”ข NumPy ๏ธ๐ŸŽ› Multilayer Perceptrons ๏ธโœ‚๏ธ Preprocessing ๐Ÿ“— Recurrent Neural Networks
๐Ÿผ 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.
๐Ÿ’ป Local Setup ๐ŸŒฒ Logging ๐Ÿณ Docker ๐Ÿค Distributed Training
๐Ÿ ML Scripts โšฑ๏ธ Flask Applications ๐Ÿšข Kubernetes ๐Ÿ”‹ Databases
โœ… Unit Tests ๐ŸŒŠ Kubeflow ๐Ÿ” 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.
๐Ÿง Attention ๐Ÿ Transformers ๐ŸŽญ Generative Adversarial Networks ๐Ÿ”ฎ Autoencoders
๐ŸŽ๏ธ Highway Networks ๐Ÿ‘น BERT, GPT2, XLNet ๐ŸŽฑ Bayesian Deep Learning ๐Ÿ•ท๏ธ Graph Neural Networks
๐Ÿ’ง Residual Networks ๐Ÿ•˜ Temporal CNNs ๐Ÿ’ Reinforcement Learning


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.
๐Ÿ“ธ Image Recognition ๐Ÿ“– Text classification ๐Ÿก Clustering โฐ Time-series Analysis
๐Ÿ–ผ๏ธ Image Segmentation ๐Ÿ’ฌ Named Entity Recognition ๐Ÿ˜๏ธ Topic Modeling ๐Ÿ›’ Recommendation Systems
๐ŸŽจ Image Generation ๐Ÿง  Knowledge Graphs ๐ŸŽฏ One-shot Learning
๐Ÿ—ƒ๏ธ Interpretability


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