deeplearning-models is an Open Source & Models tool that provides a collection of deep learning architectures and models. It offers Jupyter Notebooks for TensorFlow and PyTorch implementations, useful for AI developers and machine learning engineers.
deeplearning-models is a comprehensive collection of various deep learning architectures, models, and practical tips, presented in Jupyter Notebooks. It supports both TensorFlow and PyTorch frameworks, offering implementations for traditional machine learning, multilayer perceptrons, convolutional neural networks (including AlexNet, DenseNet, LeNet, MobileNet, VGG, ResNet), transformers, ordinal regression, normalization layers, metric learning, autoencoders (fully-connected, convolutional, variational, conditional variational), generative adversarial networks (GANs), graph neural networks (GNNs), and recurrent neural networks (RNNs). The repository also includes notebooks on model evaluation, data augmentation, tips and tricks, transfer learning, visualization, and PyTorch/TensorFlow workflows, making it an invaluable resource for learning and implementing deep learning concepts.
Best used for
Ideal for students who need to learn about deep learning architectures, implement various neural network models, and explore practical tips for TensorFlow and PyTorch. Especially valuable for those building a portfolio with real-world open-source implementations.
Common actions
implement deep learning models
learn neural networks
explore AI architectures
benchmark deep learning
face swappingautomated workflowdeepfakegithub copilotworkflows"AI Agents"open-sourcelow-code/no-codecollaboration
Capabilities
Key features
Deep learning architectures
TensorFlow models
PyTorch models
Jupyter Notebooks
Model evaluation
Data augmentation
Transfer learning
Target Audience
student
Integrations
Not yet documented
Pricing & Plans
Open Source
Free
FAQs
What deep learning frameworks are supported by deeplearning-models?
The deeplearning-models repository primarily supports both TensorFlow and PyTorch frameworks. It provides a wide array of Jupyter Notebooks demonstrating implementations and tips for models built using these popular deep learning libraries.
What types of neural network models are included in this collection?
The collection includes various neural network types such as Multilayer Perceptrons, Convolutional Neural Networks (e.g., AlexNet, ResNet, VGG), Transformers, Autoencoders, Generative Adversarial Networks (GANs), Graph Neural Networks (GNNs), and Recurrent Neural Networks (RNNs).
Are there examples for model evaluation and training techniques?
Yes, the repository offers notebooks covering model evaluation techniques like K-Fold Cross-Validation, data augmentation methods such as AutoAugment, and training tips including Cyclical Learning Rates and Gradient Clipping. It also provides PyTorch and TensorFlow workflow examples.