README.md

Deep Learning specialization, Coursera

Become a Deep Learning expert. Master the fundamentals of deep learning and break into AI.

Instructor: Andrew Ng, deeplearning.ai.

Course 1 : Neural Networks and Deep Learning

In this course, the foundations of deep learning were covered. The major learnings after completing this course were :

• Understood the major technology trends driving Deep Learning.
• Been able to build, train and apply Fully Connected Deep Neural Networks.
• Learnt how to implement efficient (Vectorized) neural networks.
• Understood the key parameters in a neural network's architecture.
• This course also taught how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

Course 2 : Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

In this 2nd course of the specialization, the fundamentals of deep learning were further explored. The major learnings after completing this course were :

• Learnt the best practices to train and develop test sets and analyze bias/variance for building deep learning applications.
• Understood how to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking.
• Learnt how to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
• Understood the implementation of a neural network in TensorFlow.

License

This repository is released under the MIT license.