Deep Learning Models by Sebastian Raschka

A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks:
- Traditional Machine Learning
- Multilayer Perceptrons
- Convolutional Neural Networks
- Basic
- Concepts
- AlexNet
- DenseNet
- Fully Convolutional
- LeNet
- MobileNet
- Network in Network
- VGG
- ResNet
- Transformers
- Ordinal Regression and Deep Learning
- Normalization Layers
- Metric Learning
- Autoencoders
- Fully-connected Autoencoders
- Convolutional Autoencoders
- Variational Autoencoders
- Conditional Variational Autoencoders
- Generative Adversarial Networks (GANs)
- Graph Neural Networks (GNNs)
- Recurrent Neural Networks (RNNs)
- Many-to-one: Sentiment Analysis / Classification
- Many-to-Many / Sequence-to-Sequence
- Model Evaluation
- K-Fold Cross-Validation
- Data Augmentation
- Tips and Tricks
- Transfer Learning
- Visualization and Interpretation
- PyTorch Workflows and Mechanics
- PyTorch Lightning Examples
- Custom Datasets
- Training and Preprocessing
- Improving Memory Efficiency
- Parallel Computing
- Other
- Autograd
- TensorFlow Workflows and Mechanics
- Custom Datasets
- Training and Preprocessing
- Related Libraries

Link: GitHub

Navigational hashtags: #armtutorials
General hashtags: #ml #machinelearning #dl #deeplearning #pytorch #tensorflow #tf #pytorchlightning

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