Deep Learning Specialization

While undergoing my internship this year, i stumbled unto a course being paraded on my favorite MOOC platform, Coursera.

Deep Learning by Andrew Ng is a specialization that intrigued me. I had previous experience with the world of traditional machine learning algorithms (shout out to linear regression) but for some unknown reason, the hype seemed to be around deep learning wherever i turned. Then with a little poking around i came to see that whenever data was available, deep learning became an entirely different beast.

With that being established, i set about to learn all i could about deep learning through the specialization. It was going to cost money I did not have to spare at the moment (approximately $200) but then in swooped Coursera's financial aid scheme to my rescue

The objective of the Specialization is to learn the foundations of Deep Learning, including how to build neural networks, lead machine learning projects, and quite a bit more (like: convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization).

5 Course List

1.  Neural Networks and Deep Learning

2. Improving Deep Neural Networks

3. Structuring Machine Learning Projects

4. Convolutional Neural Networks

5. Sequence Models

Projects Overview

Throughout the Specialization, students are asked to complete numerous projects such as deep learning models in healthcare, autonomous driving, sign language reading, music generation, and natural language processing. I learnt so much from the theory governing the rise of AI to the legends behind the scenes (special shout-out to Geoffrey Hinton my idol) and even how to achieve better performances on existing systems.

Certificates

I completed the entire specializations and the 6 certificates obtained can be viewed on google drive

© 2019 Domnan Diretnan @deven96
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