sdsawtelle / coursera-deep-learning-specialization

A collection of course notes and code snippets for the 5-course Deep Learning specialization on Coursera.

coursera-deep-learning-specialization

A collection of course notes and code snippets for the 5-course Deep Learning specialization on Coursera, developed by deeplearning.ai with the marvelous Andrew Ng. I completed the specialization in July 2019 and my review of the series can be found here.

In this repo each of the five courses has a folder containing:

• a jupyter notebook with notes (and images) from the lecture / homework content, where I give a succinct note summary for each section of content.
• a code-base folder defining functions or code snippets from the homework that I found particularly illuminating, with its own README file annotating the code organization.

The notebook deep-learning-resources is just a place for me to collect links to interesting or useful resources - it is a work in progress.

I did not include the homework notebooks and associated jupyter workspaces in this repo, to respect the Coursera paywall.

Listing of Course Notebooks

NOTE: I use custom CSS for image insertion and some other formatting, which is sanitized by the github notebook previewer (see this article). To get a complete preview of my notebooks you can click on the pokeball-looking icon at the top right of the github rendered preview, which will open nbviewer in your browser

neural-nets-and-deep-learning - An intro to neural networks progressing from logistic regression as an NN, to shallow NNs, to deep L-layer NNs.

improving-deep-neural-networks - Various important topics in working with NNs including bias/variance, regularization, optimization methods and backprop considerations and hyperparameter tuning. Also introduced tensorflow + keras.

structuring-machine-learning-projects - Tips and insights on how to organize and execute a DL project (with many ideas applicable to general ML projects). Covers metrics, train/dev/test splitting, the role of human performance level, how to conduct error analysis, data mismatch between train and dev/test sets, and harnessing transfer learning.

convolutional-neural-networks - Basic architecture of CNNs including plain-english interpretation and intuition for the role of the different layers / sub-architectures. Survey of some "classic" architectures like AlexNet. Overview of specific architectures like ResNets and Inception and specific use cases like object detection and face recognition. Also covers data augmentation.

sequence-models - Basic architecture of RNNs including plain-english interpretation and intuition for the role of the different layers / sub-architectures. Introduction to, and applications of, embeddings. Overview of specific use cases in sequence-to-sequence architectures.