Learn to Build a Machine Learning Application from Top Articles of 2017
For Jan-Dec 2017, we compared nearly 20,000 articles about creating a machine learning application and picked the Top 50.
“Hiring a Machine Learning engineer or Data Scientist in Silicon Valley is becoming like hiring a professional athlete. That’s how demanding it is” — The New York Times
Machine Learning has become one of the most demanding skills in the workforce today, with the average salary in US reaching $134,472 (source: Indeed).
We believe learning from data scientists who have hands-on experience in the field is a great way to advance your career. This directory is designed to make your life easier as it organizes the most useful articles written in 2017, where experienced data scientists share their lessons in building and shipping a machine learning application.
As an article ranking service for professionals, we take “quality” very seriously and make sure each article you read is exceptionally good. This is an extremely competitive directory with only 50 articles selected out of 20,000 (0.25%). Mybridge AI is constantly evolving as it ranks articles by the total number of shares & reading time and by our own machine learning algorithm.
This directory has 15 key topics as shown below.
If you’re a beginner who are just getting started, we recommend these courses below,
The Beginner’s Guide to Building an Artificial Intelligence in Unity.
[5,041 recommends, 4.7/5 stars]
Deep Learning and Computer Vision A-Z
: Learn OpenCV, SSD & GANs and create image recognition apps.
[8,161 recommends, 4.5/5 stars]
If you’re looking for open source projects,
- Machine Learning Projects of the Year (avg. 3,558
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(Click the numbers below. The numbers are NOT ordered by ranking in this post)
If you like a text version without images: Go to our Github link
<Image Manipulation>
No 1
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. Courtesy of NVIDIA AI and UC Berkeley
No 2
Using Deep Learning to Create Professional-Level Photographs. Courtesy of Hui Fang at Google Research
No 5
Deep Photo Style Transfer: A deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
No 6
Deep Image Prior: Super-resolution, inpainting, denoising without learning on a dataset and pretrained networks. Comparable results to learned methods. Courtesy of Dmitry Ulyanov
<Image Classification>
No 7
Feature Visualization: How neural networks build up their understanding of images. Courtesy of Chris Olah and Ludwig Schubert at Google Brain
No 8
An absolute beginner’s guide to Image Classification with Neural Networks [4481 stars on Github]. Courtesy of David Humphrey at Mozilla Firefox
<Face Recognition>
No 10
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression. Courtesy of Aaron Jackson
<Video Stabilization>
No 13
Fused Video Stabilization on the Pixel 2 and Pixel 2 XL. Chia-Kai Liang at Google Research
<Object Detection>
No 14
How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow and Keras. Coutesy of Tim Anglade
No 19
Recognizing Traffic Lights With Deep Learning: How I learned deep learning in 10 weeks and won $5,000. Courtesy of David Brailovsky and freeCodeCamp
<Recommendation Al>
No 20
Spotify’s Discover Weekly: How machine learning finds your new music. Courtesy of Sophia Ciocca and Hackernoon
<Gaming Al>
No 22
MariFlow — Self-Driving Mario Kart w/Recurrent Neural Network. Courtesy of SethBling
No 23
OpenAI Baselines: DQN. Reproduce reinforcement learning algorithms with performance on par with published results. Courtesy of OpenAI
No 27
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI. Courtesy of Stanford University
<Chess Al>
No 29
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Courtesy of Deepmind
<Speech Al>
No 37
Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model — Data Scientists at Google.
No 42
Make your own music with WaveNets: Making a Neural Synthesizer Instrument. Courtesy of Jesse Engelberg
<Natural Language Processing>
No 43
Learning to communicate: Agents developing their own language — OpenAI Research.
No 44
Big Picture Machine Learning: Classifying Text with Neural Networks and TensorFlow. Courtesy of Déborah Mesquita
<Prediction>
No 47
Using Machine Learning to Predict Value of Homes On Airbnb. Courtesy of Robert Chang, Data at Aribnb
If you like a text version without images: Go to our Github link
That’s it for Machine Learning Articles of the Year. If you like this curation, read best daily articles based on your programming skills on our website.