-
๐ โ https://practicalai.me
-
๐ Illustrative ML notebooks in TensorFlow 2.0 + Keras.
-
โ๏ธ Build robust models using the functional API w/ custom components
-
๐ฆ 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.
|
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.
|
๐ป Local Setup |
๐ฒ Logging |
๐ณ Docker |
๐ค Distributed Training |
๐ ML Scripts |
โฑ๏ธ Flask Applications |
๐ข Kubernetes |
๐ Databases |
โ
Unit Tests |
|
๐ Kubeflow |
๐ Authentication |
|
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.
|
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 |
|
|
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.
|
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 |
|
๐ฏ One-shot Learning |
|
|
|
๐๏ธ Interpretability |
๐ฌ Newsletter - Subscribe to get updates on new content.