· Lessons

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Notebooks

• 📚 Illustrative ML notebooks available in both TensorFlow 2.0 + Keras and PyTorch.
  • Should I pick TensorFlow or PyTorch? Choice of framework doesn’t matter! Check out the basic lessons and choose what you find more intuitive/suitable but the most important thing is to work on projects and share them with the community.
  • Do I need to know both TensorFlow or PyTorch? It is very important to at least know how to read both frameworks because cutting edge research continues to use both frameworks. Luckily, they're both very easy to learn and very easy to rewrite in the other framework.
• 💻 These are not a set of tutorials where we just load a bunch of packages and apply it on preloaded datasets. We explain every concept in the notebooks with clean code, simple math and visualizations to make them as intuitive as possible.
• 📓 If you prefer Jupyter Notebooks or want to add/fix content, check out the notebooks directory.

Foundation

Learn Python basics with notebooks. Use data science libraries like NumPy and Pandas. Learn the basics of deep learning frameworks like TensorFlow and PyTorch.	📓 Notebooks	🐍 Python	🔢 NumPy
	🐼 Pandas	TensorFlow	PyTorch

Basics

Implement basic ML models in TensorFlow 2.0 + Keras or PyTorch. Create deep learning models for improved performance.	📈 Linear Regression |	📊 Logistic Regression |	️🎛 Multilayer Perceptrons |
	🔎 Data & Models |	🛠 Utilities |	️✂️ Preprocessing |
	️🖼 Convolutional Neural Networks |	👑 Embeddings |	📗 Recurrent Neural Networks |

APIs

Create a RESTful ML application using Fast API to create applications. Perform unit tests on ML functions and implement appropriate logging throughout the application. Walk through modeling and set fallbacks for inference in production.

🏎 APIs (releasing very soon) | |

Full-stack

Learn how to collect data and organize it using SQL. Showcase your applications using a simple Boostrap front-end.

🌍 Web scraping

🔋 SQL

🎨 Bootstrap

Scaling

Standardize and scale your ML applications with Docker and Kubernetes. Deploy simple and scalable ML workflows using MLFlow.

🐳 Docker

🚢 Kubernetes

🌊 MLFlow

Advanced

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	📘 Language Modeling	🤗 Transformers	🤯 SHA-RNN
	🎭 Generative Adversarial Networks	🔮 Autoencoders	🕷️ Graph Neural Networks	⏱ Temporal CNNs
	🍒 Reinforcement Learning	🎯 One-shot Learning	🎱 Bayesian Deep Learning	🐙 Causal Inference

Topics

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	🖼️ Image Segmentation	🎨 Image Generation
	📖 Text classification	💬 Named Entity Recognition	🧠 Knowledge Graphs
	🏘️ Topic Modeling	🍡 Clustering	🕵️ Anomaly Detection

Miscellaneous

Learn about miscellaneous topics that are at the forefront of ML research and application.	⏰ Time-series	🎤 Speech Recognition	🛒 Recommendation Systems
	🗃️ Interpretability	✂️ Model Compression	✍️ Data Annotation
	⚖️ Imbalanced Datasets	👻 Missing Values	📊 Data Visualization

Statistical Learning

Learn the basics of statistics that paved the way for all the topics above. Implement statistical learning methods in scikit-learn.	🧪 Hypothesis Testing	❤️ Maximum Likelihood Estimation	👶 Naive Bayes
	📈 Linear Regression	📊 Logistic Regression	🦺 Support Vector Machines
	🌳 Random Forests	🏘 Nearest Neighbors	🍿 Gaussian Processes
	🥅 Matrix Decomposition	🎩 Hidden Markov Models	🦠 Survival Analysis