1. Study of Deep learning Packages: Tensorflow, Keras, Theano and PyTorch. Document the distinct features and functionality of the packages.
2. Implementing Feedforward neural networks with Keras and TensorFlow
   * Import the necessary packages
   * Load the training and testing data (MNIST/CIFAR10)
   * Define the network architecture using Keras
   * Train the model using SGD
   * Evaluate the network
   * Plot the training loss and accuracy
3. Build the Image classification model by dividing the model into following 4 stages:
   * Loading and preprocessing the image data
   * Defining the model’s architecture
   * Training the model
   * Estimating the model’s performance
4. Use Autoencoder to implement anomaly detection. Build the model by using:
   * Import required libraries
   * Upload / access the dataset
   * Encoder converts it into latent representation
   * Decoder networks convert it back to the original input
   * Compile the models with Optimizer, Loss, and Evaluation Metrics
5. Implement the Continuous Bag of Words (CBOW) Model. Stages can be:
   * Data preparation
   * Generate training data
   * Train model
   * Output 
