Deep Learning specialization, Coursera
Instructor: Andrew Ng, deeplearning.ai.
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.
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.
This repository is released under the MIT license.