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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.
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๐ Notebooks |
๐ Linear Regression |
๐ Data & Models |
๏ธ๐ผ Convolutional Neural Networks |
๐ Python |
๐ Logistic Regression |
๐ Utilities |
๐ Embeddings |
๐ข NumPy |
๏ธ๐ Multilayer Perceptrons |
๏ธโ๏ธ Preprocessing |
๐ Recurrent Neural Networks |
๐ผ Pandas |
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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.
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๐ป Local Setup |
๐ฒ Logging |
๐ณ Docker |
๐ค Distributed Training |
๐ ML Scripts |
โฑ๏ธ Flask Applications |
๐ข Kubernetes |
๐ Databases |
โ
Unit Tests |
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๐ Kubeflow |
๐ Authentication |
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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.
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๐ง Attention |
๐ Transformers |
๐ญ Generative Adversarial Networks |
๐ฎ Autoencoders |
๐๏ธ Highway Networks |
๐น BERT, GPT2, XLNet |
๐ฑ Bayesian Deep Learning |
๐ท๏ธ Graph Neural Networks |
๐ง Residual Networks |
๐ Temporal CNNs |
๐ Reinforcement Learning |
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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.
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๐ธ Image Recognition |
๐ Text classification |
๐ก Clustering |
โฐ Time-series Analysis |
๐ผ๏ธ Image Segmentation |
๐ฌ Named Entity Recognition |
๐๏ธ Topic Modeling |
๐ Recommendation Systems |
๐จ Image Generation |
๐ง Knowledge Graphs |
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๐ฏ One-shot Learning |
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๐๏ธ Interpretability |
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