/TensorflowML

Welcome to a comprehensive repository dedicated to implementing a wide array of algorithms in Machine Learning, Deep Learning, and Reinforcement Learning. This repository emphasizes the use of TensorFlow 2, but also incorporates various other libraries to deliver robust and versatile implementations across multiple domains.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Introduction:

A repository dedicated to implement many sorts of algorithms in Machine Learning, Deep Learning and Reinforcement Learning using TensorFlow 2 (emphasising heavily in this library) and many other libraries.

Algorithms:

[x] Machine Learning:

  • Linear Regression
  • Kernel Linear Regression
  • Logistic Regression (0,1)
  • Logistic Regression (-1,1)
  • Kernel Logistic Regression
  • K Nearest Neighbours
  • K Means Clustering
  • Naive Bayes Classifier
  • Linear Support Vector Machine
  • Non Linear Support Vector Machine

[x] Computer Vision:

  • Auto-encoders
    • Vanilla Autoencoder
    • Convolutional Autoencoder
    • Sparse Autoencoder
    • Stacked Autoencoder
    • Denoise Autoencoder
    • Variational Autoencoder
    • Contractive Autoencoder
    • Adversarial Autoencoder
    • Self-attention Autoencoder
    • Recurrent Autoencoder
    • Sparse Variational Autoencoder
    • Beta Variational Autoencoder
    • Transformative Autoencoder
  • Generative Adversarial Network
    • Deep ANN GAN
    • Deep Convolutional GAN
    • Conditional GAN
    • Least Square GAN
    • Wasserstein GAN
    • Wasserstein Improved GAN
    • Deep Regret Analytic GAN
    • Cycle GAN
    • Star GAN
    • Super Resolution GAN
  • Image Classification
    • Large Network
      • AlexNet
      • VGG Net
      • Inception Net
        • Inception V1 (Google Net)
        • Inception V2
        • Inception V3
        • Inception V4
      • Residual Net
      • Res Next
      • SeNet
      • Dense Net
      • Highway Net
      • Network In Network
      • Vision Transformer
      • Residual Attention Net
      • Capsule Network
    • Small Network
      • LeNet
      • ZFNet
      • Deep Compression
      • Knowledge Distillation
      • MLP-Mixer
      • Mobile Net
      • Poly Net
      • Shuffle Net
      • Squeeze Net
      • Xception Net
      • XNOR-Net
    • Robustness
      • Adversarial Saliency Maps
      • Black Box Methods
      • Fast Gradient Sign Method
      • Iterative Least Likely Method
  • Miscellaneous
    • Genetic CNN
    • Style Transfer
    • Empirical Risk Minimisation
    • Gaussian CDF
    • Gradient Accumulation
    • Gradient test for Initialisation
    • Regularisation with Nelder Mead Optimisation

[x] Reinforcement Learning:

  • Memory:
    • Replay Buffer
    • Non Buffer
    • Prioritised Experience Replay
  • Strategies:
    • Value Based:
      • DQN
      • Double DQN
      • Duelling DQN
      • Double Duelling DQN
      • Rainbow
      • C51
    • Policy Based:
      • PG (vanilla)
      • Reinforce
      • Trusted Region PO (TRPO)
      • Proximal PO (PPO)
      • ACKTR
    • Actor Critic Based:
      • Vanilla AC
      • Advantageous AC (A2C)
      • Asynchronous Advanatageous AC (A3C)
      • Soft AC (SAC)
      • Deep Deterministic PG (DDPG)
      • Twin Delayed DDPG (TD3)

Contributions

Contributions are welcome! If you have implemented a new insights or have improvements to existing implementations, feel free to submit a pull request. Please follow the contribution guidelines outlined in the repository.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Happy generating!