Improving-Deep-Neural-Networks

This Repository contains the Programming assignments completed under second course of Deep Learning specialisation(Coursera)

Skills learnt

Best practices to train and develop test sets
Analyzing bias/variance for building deep learning applications
Using standard Neural Network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking
Implementation and application of variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam
Checking for optimization algorithm's convergence and implementation a Neural Network in TensorFlow