Deep-Learning-KERAS

How neural networks feed data forward through the network. The gradient descent algorithm and how variables are optimized with respect to a defined function. Backpropagation and how neural networks learn and update their weights and biases. The vanishing gradient problem. Activation Functions. Deep learning libraries namely, Keras, PyTorch, and TensorFlow. Building a regression model using the Keras library. Building a classification model using the Keras library. The difference between the shallow and deep neural networks. Convolutional networks and how to build them using the Keras library. Recurrent Neural Networks. Autoencoders and how to build them using the Keras library.

1. Artificial Neural Networks

• Forward Propagation: I built a neural network from scratch and code how it performs predictions using forward propagation.

2. Shallow Models with Keras

• Regression: Making Regression Neural Model using Keras on a sample data.

• Classification: Making a Classification Neural Model using Keras and predicing data on MNIST database. The MNIST database, short for Modified National Institute of Standards and Technology database, is a large database of handwritten digits that is commonly used for training various image processing systems.he database is also widely used for training and testing in the field of machine learning. The MNIST database contains 60,000 training images and 10,000 testing images of digits written by high school students and employees of the United States Census Bureau.

3. Convolutional Neural Network (CNN)

• Convolutional Neural Network with One & Two Convolutional and Pooling Layers using MNIST database.