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

A practical approach to machine learning.

Notebooks

🌎 → https://practicalai.me
📚 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
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

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Name	Latest commit message	Commit time
Failed to load latest commit information.
data	fixed tumor data labels to match with plot colors	Nov 8, 2019
notebooks/basic_ml	changed notebooks org for production lessons	Jan 6, 2020
.gitignore	update gitignore and codeowners	Oct 2, 2019
CHANGES.md	All basic ML notebooks in TensorFlow 2.0 + Keras are complete	Dec 3, 2019
CODEOWNERS	update gitignore and codeowners	Oct 2, 2019
CODE_OF_CONDUCT.md	added notebooks lesson	Oct 2, 2019
CONTRIBUTING.md	added notebooks lesson	Oct 2, 2019
LICENSE	All basic ML notebooks in TensorFlow 2.0 + Keras are complete	Dec 3, 2019
README.md	fixed notebook links	Jan 31, 2020
requirements.txt	fixed large images in github notebook version	Oct 3, 2019