Top 10 curated AI reads for October 2018
October, 2018
News about Machine Learning (ML), Artificial Intelligence (AI), Data Science (DS) and related advanced analytics areas.
Welcome to xplore.ai’s forth post of the 10 curated AI reads monthly series. The objective of these series is to provide the audience with a curated list of the most interesting news, publishings and tools that our team have ran into during the previous month.
Computer Vision
1. What-If Tool for Tensorflow
A new feature of the open-source TensorBoard web application, which let users analyze an ML model without writing code. Given pointers to a TensorFlow model and a dataset, the What-If Tool offers an interactive visual interface for exploring model results. Although it applies to modeling anything in TF, we think Computer Vision is the most interesting application
2. GAN Lab
An Interactive, Visual Experimentation Tool for Generative Adversarial Networks understanting. We also liked the textual explanation bellow.
Machine Learning
3. New Pandas-like features in Scikit-Learn
We are excited to see that managing feature creation and transformation will be from now on simpler, more feature-rich, robust, and standardized. Scikit-Learn 0.20 looks so promising! Thanks to the community for such advances.
4. Forecasting at Uber (I)
“This article is the first in a series dedicated to explaining how Uber leverages forecasting to build better products and services”. At xplore.ai we are working on forecasting problems in the travel (hotel) industry and completely agree with the “Prediction intervals are just as important as the point forecast itself and should always be included in your forecasts” statement.
5. Algorithmic Marketing Book
Very complete book that covers the main areas of marketing that require micro-decisioning — targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.
Reinforcement Learning
6. DQN RL Flappy Bird game
PyTorch reinforcement learning tutorial, the author showed how a computer can learn to play Flappy Bird without any previous knowledge about the game, using only a trial-and-error approach as a human would do when encountering the game for the first time.
7. 60 days Reinforcement Learning Challenge
Learn Deep Reinforcement Learning in Depth in 60 days
8. Usage of Embeddings at OpenAI five
OpenAI Five (Dota 2 AI player) aspect study of their network architecture — their inventive use of embeddings to handle a huge and variable number of policy inputs and outputs. While the use of embeddings and dot product for attention are standard techniques in natural language processing, they are not widely used in reinforcement learning.
Bayesian methods
9. Summer School Deep|Bayes
The Bayesian Methods Research group is one of the leading machine learning research groups in Russia. Members of the group have developed a range of university courses in Bayesian Methods, Deep Learning, Optimization and Probabilistic Graphical Models. You can find all the videos, slides and assignments open sourced in their website
Natural Language Processing
10. Key topics extraction and contextual sentiment of users’ reviews
Key topics extraction and contextual sentiment of users’ reviews using Spacy + NLTK (for VaderSentiment) + Sklearn + difflib. Pretty simple yet cool pipeline for such task
And this is it for what we found out to be interesting during September. At xplore.ai, we are always trying out the latest tools, experimenting with cutting edge algorithms and reading about the latest trends in every industry where data is generating unprecedented value.
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