Backstory
(Last Updated: June, 2017) If you’re here without context, let me bring you up to speed. Early 2017 I quit my job in order to study full time to pursue a learning path into the emerging field of Deep Learning. To that end, I put together a self-study plan which you can read more about on Medium and an updated/revised version on LinkedIn.
My study plan covered a few large “pieces” of study material, mainly the Fast.ai MOOC and a benchmark project. However, since starting, I’ve expanded my studies for a number of reasons which I’ll leave for another blog article. The material I’ve covered so far is listed below and will be updated as I continue my progress into the field of deep learning.
If you’re starting your journey and looking for examples of a study path and material to use, hopefully this will help.
My Deep Learning List
(The below list does not represent articles and blogs I’ve “glanced over”, only those I’ve spend considerable amount of time reading and attempting to understand.)
Course
- Deep Learning - Part 1: MOOC version and USF, Data Institute: Deep Learning - Part 1 from the company Fast.ai
- Data Scientist with Python track - DataCamp
- Machine Learning by Stanford University - Coursera
Videos
Books, Papers, Articles & Blogs
- Neural Network Architectures
- A Neural Network in 11 lines of Python
- Standford CS231n: Convolutional Neural Networks for Visual Recognition
- Grokking Deep Learning
- Designing great data products
- Get Started with TensorFlow
- Deep MNIST for Experts
- TensorFlow Machancis 101
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Breaking Linear Classifiers
- Explaining and Harnessing Adversarial Examples
- How to trick a neural network into thinking a panda is a vulture
- Attacking Machine Learning with Adversarial Examples
- GAN by Example using Keras on Tensorflow Backend
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- A Neural Algorithm of Artistic Style
- Convolutional Arithmetic Tutorial