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CourseDeep LearningLast updated 1 day ago

A beginner's guide to the fundamentals of deep learning

Tutorials have been chosen to maximize learning curve, i.e. learn the most in the shortest amount of time. Tutorials cover introductory topics for deep learning!

Expected time to completion: 2-3 weeks (10-15 sessions)

Pre-requisites: Calculus (specifically, differentiation)

Pro-tip: If you want to skip sections or tutorials, mark them as completed.

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Machine Learning basics: what it is, gradient descend

A Visual Introduction (and Review) to Machine Learning
4 mins
What is Machine Learning? Why Machine Learning?
9 mins
Perceptron algorithm
4 mins
Tutorial: Linearly separable data
2 mins
Quiz: Perceptron algorithm
6 mins

Deep Learning basics: what it is, neural networks, computational graphs, back-propagation

Deep Learning in a Nutshell
32 mins
A Neural Network Playground
2 mins
Gradient Descent intuition
2 mins
Calculus on Computational Graphs: Backpropagation -- colah's blog
2 mins
Andrej Karpathy: Yes you should understand backprop
2 mins

Convolutional Neural Networks: the most successful architecture for computer vision

Recurrent Neural Networks: the most successful architecture for natural language processing, and other domains with sequential input

A Deep Dive into Recurrent Neural Nets
21 mins
Understanding LSTM Networks -- colah's blog
2 mins

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A Visual Introduction (and Review) to Machine Learning

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About the contributors:

Keshav DhandhaniaDeep Learning @ MIT (2014). DM me for 1-on-1 mentorship (paid).

34%

Nikhil BudumaCo-founder @ Remedy. Bachelor's and Master's @ MIT Computer Science.

30%

Adit DeshpandeML Blog: https://adeshpande3.github.io/

24%

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