README.bkp

# Machine-Learning-with-Tensorflow-2
A repository comprising the algorithms of Machine Learning implemented on tensorflow 2 with keras and many other special machine learning libraries. If you find this useful please share and give your valuable insights.

```
├── ComputerVision
│   ├── AutoEncoders
│   │   ├── ContractiveAutoencoder
│   │   ├── ConvolutionalAutoEncoder
│   │   ├── DenoiseAutoEncoder
│   │   ├── SparseAutoEncoder
│   │   ├── StackedAutoEncoder
│   │   ├── VanillaAutoencoder
│   │   └── VariationalAutoEncoder
│   ├── GAN
│   │   ├── ConditionalGan
│   │   ├── CycleGAN
│   │   ├── DeepConvolutionalGAN
│   │   ├── DeepRegretAnalyticGAN
│   │   ├── InitialGAN
│   │   ├── LeastSquareGan
│   │   ├── StarGAN
│   │   ├── SuperResolutionGAN
│   │   ├── WassersteinGAN
│   │   └── WassersteinImprovedGAN
│   ├── ImageClassification
│   │   ├── LargeNetwork
│   │   │   ├── AlexNet
│   │   │   ├── CapsuleNetwork
│   │   │   ├── DenseNet
│   │   │   ├── HighwayNet
│   │   │   ├── InceptionNet
│   │   │   │   ├── InceptionV1
│   │   │   │   └── InceptionV2
│   │   │   ├── NetworkInNetwork
│   │   │   ├── ResidualAttentionNet
│   │   │   ├── ResNet
│   │   │   ├── ResNext
│   │   │   ├── SENet
│   │   │   ├── VGGNet
│   │   │   └── VisionTransformer
│   │   ├── Robustness
│   │   │   ├── AdversarialSaliencyMaps
│   │   │   ├── BlackBoxAttacks
│   │   │   ├── FastGradientSignMethod
│   │   │   └── IterativeLeastLikelyMethod
│   │   └── SmallNetwork
│   │       ├── DeepCompression
│   │       ├── FractalNet
│   │       ├── KnowledgeDistillation
│   │       ├── Lenet5
│   │       ├── MLP-Mixer
│   │       ├── MobileNet
│   │       ├── PolyNet
│   │       ├── ShuffleNet
│   │       ├── SqueezeNet
│   │       ├── XceptionNet
│   │       ├── XNOR-Net
│   │       └── ZFNet
│   └── Misc
│       ├── GeneticCNN
│       └── StyleTransfer
├── Machine_Learning_Algorithms
│   ├── KNearestNeighbour
│   ├── LinearRegression
│   ├── LogisticRegression
│   ├── NaiveBayesClassifier
│   └── SupportVectorMachine
└── Miscellaneous
    ├── EmpiricalRiskMinimization
    ├── GaussianCDF
    ├── GradientAccumulation
    ├── Initialization_and_gradient_test
    └── Regularization_with_NM_optimization



```

Before trying, follow these steps:

  Install the neccessary libraries and modules
   - Activate your *virtualenv*
   - Run the following command in your terminal(LINUX) or cmd(WINDOWS)
 
  ###  Installation process :
  ```bash
  pip3 install tensorflow[and-cuda] #if you've an nvidia-gpu with cuda support
  pip3 install pandas
  pip3 install numpy
  pip3 install matplotlib
  pip3 install sklearn
  pip3 install tensorflow_probability
  ```
  
  ## Fire up your terminal/cmd
  ```bash
  sh setup.sh
  ```
   
   # 🤖 Linux or Windows:
   ```bash
   python3 <file_name>.py
   ```

# 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!