In this first course of the Deep Learning Specialization, I studied the foundational concept of neural networks and deep learning.

Made me familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

In this second course of the Deep Learning Specialization, I understood the processes that drive performance and generate good results systematically.

Learnt the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, and implement a neural network in TensorFlow.

In this third course of the Deep Learning Specialization, I learnt how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

I was be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.