Are you new to data science? Find here our Beginner’s Guide.
What does make America great? A scientist’s perspective, by Dj Patil.
The Data Science Renaissance, by Rob Thomas.
Chapter 1: Basic concepts
How to begin your own data science journey! by Shreyas Raghavan.
How to build a data science pipeline, Balázs Kégl.
I have data. I need insights. Where do I start? by Rama Ramakrishnan.
Chapter 2: Data Visualization
If Taxi Trips were Fireflies: 1.3 Billion NYC Taxi Trips Plotted, by Ravi Shekhar.
What coding skills do devs want to develop by Hannah Yan Han.
Data Curious: A roundup of data stories, datasets and visualizations from last week, by Benjamin Cooley.
Chapter 3: Machine Learning Algorithms
Reducing Dimensionality from Dimensionality Reduction Techniques, by Elior Cohen.
Boosting the accuracy of your Machine Learning models, by Prashant Gupta.
Types of Optimization Algorithms used in Neural Networks and Ways to Optimize Gradient Descent, by Anish Singh Walia.
Types of Machine Learning Algorithms You Should Know, by David Fumo.
To err is algorithm: Algorithmic fallibility and economic organisation, by Juan mateos-garcia.
A candid conversation about machine learning with Madison May, by Ryan Louie.
Take a Break!
You Can’t Make This Stuff Up… Or, Can You? by ericcolson.
Building a Real-Time Object Recognition App with Tensorflow and OpenCV, by Dat Tran.
Chapter 4: Deep Learning
Neural Network Architectures, by Eugenio Culurciello.
Introducing Deep Learning and Neural Networks — Deep Learning for Rookies, by Nahua Kang.
Linear algebra cheat sheet for deep learning, by Brendan Fortuner.
Chapter 5: Real Applications
Smart Cities and Image Recognition, by Joe Hanson.
Redefining Basketball Positions with Unsupervised Learning, by Evan Baker.
Tensorflow RMSD: Using Tensorflow for things it was not designed to do, by Pande Lab at Stanford University.
Chapter 5: What’s Next
Functional programming for deep learning, by Joyce Xu.
Notes on the Cramer GAN, by Arthur Gretton.
GANGogh: Creating Art with GANs, by Kenny Jones.