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Basic ML |
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Basics | Machine Learning | Tools | Deep Learning | |
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📓 Notebooks | 📈 Linear Regression | 🔎 Data & Models | ️🖼 Convolutional Neural Networks |
🐍 Python | 📊 Logistic Regression | 🛠 Utilities | 👑 Embeddings | |
🔢 NumPy | ️🎛 Multilayer Perceptrons | ️✂️ Preprocessing | 📗 Recurrent Neural Networks | |
🐼 Pandas |
Production ML |
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Local | Applications | Scale | Miscellaneous | |
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💻 Local Setup | 🌲 Logging | 🐳 Docker | 🤝 Distributed Training |
🐍 ML Scripts | ⚱️ Flask Applications | 🚢 Kubernetes | 🔋 Databases | |
✅ Unit Tests | 🌊 Kubeflow | 🔐 Authentication |
Advanced ML |
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General | Sequential | Popular | Miscellaneous | |
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🧐 Attention | 🐝 Transformers | 🎭 Generative Adversarial Networks | 🔮 Autoencoders |
🏎️ Highway Networks | 👹 BERT, GPT2, XLNet | 🎱 Bayesian Deep Learning | 🕷️ Graph Neural Networks | |
💧 Residual Networks | 🕘 Temporal CNNs | 🍒 Reinforcement Learning |
Topics |
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Computer Vision | Natural Language | Unsupervised Learning | Miscellaneous | |
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📸 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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