Top 10 roles in AI and data science
0 Data Engineer
1 Decision-Maker
2 Analyst
3 Expert Analyst
4 Statistician
5 Applied Machine Learning Engineer
6 Data Scientist
7 Analytics Manager / Data Science Leader
8 Qualitative Expert / Social Scientist
9 Researcher
10+ Additional personnel

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The Fundamentals of #Matplotlib

Having a good grasp of these basics will greatly ease your foray into the expansive world of data visualization.

Link to the article

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Postdoctoral / Research Staff Member Position at IBM Research (Zurich) on Unifying Learning and Reasoning

Post-doc position in machine listening/Inria Nancy -- Grand Est, France

PhD Position in Deep Learning for Robotics at Istanbul Technical University (Turkey), in collaboration with Halmstad University (Sweden)

Positions in Machine Learning and Game Playing

Robot/Reinforcement/Deep Learning Research Associate Positions in Cyprus

#Job

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Seems tolch.av used GANtools to generated this interesting GIF.

#GANs
#GenerativeAI

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A new tool uses #AI to spot text written by AI

AI algorithms can generate #text convincing enough to fool the average human—potentially providing a way to mass-produce fake news, bogus reviews, and phony social accounts. Thankfully, AI can now be used to identify fake text, too.

Link to the article

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Keras Tuner is a new #Keras wrapper that uses #sklearn grid search for tunning the hyperparameters of #deepLearning models

Link to Tuner documentation

Link to a related article

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How to Develop a #Pix2Pix #GAN for Image-to-Image Translation

Link to the paper

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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

Link to the main paper

Link to a related article

#MuZero
#DeepMind
#ReinforcementLearning

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OpenAI releases Safety Gym for reinforcement learning

To study constrained #RL for safe exploration, we developed a new set of environments and tools called #SafetyGym. By comparison to existing environments for constrained RL, Safety #Gym environments are richer and feature a wider range of difficulty and complexity.

Link to the Safety Gym

Link to a related article

#OpenAI
#ReinforcementLearning

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A lot of #DataScience Cheatsheets such as #DeepLearning #Python #Docker

Link to the Github repo

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#DeepLearning with #PyTorch

Download a free copy of the book and learn how to get started with #AI / #ML development using PyTorch

#Python

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#MachineLearning for Scent: Learning Generalizable Perceptual Representations of Small Molecules

Predicting the relationship between a molecule’s structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, impacting human nutrition, manufacture of synthetic fragrance, the environment, and sensory neuroscience. We propose the use of graph neural networks for QSOR, and show they significantly outperform prior methods on a novel data set labeled by olfactory experts. Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated by a strong performance on two challenging transfer learning tasks. Machine learning has already had a large impact on the senses of sight and sound. Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.

Link to the paper by #Google Research and ...

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3 Ways to Encode Categorical Variables for #DeepLearning

The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods

Link to the article

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#NVIDIA Makes 3D #DeepLearning Research Easy with #Kaolin #PyTorch Library

At its core, Kaolin consists of an efficient suite of geometric functions that allow manipulation of 3D content. It can wrap into PyTorch tensors 3D datasets implemented as polygon meshes, point clouds, signed distance functions or voxel grids.

With their 3D dataset ready for deep learning, researchers can choose a neural network model from a curated collection that Kaolin supplies. The interface provides a rich repository of models, both baseline and state of the art, for classification, segmentation, 3D reconstruction, super-resolution and more.

Link to the article

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What Comes After Deep Learning

Link to the article

#DeepLearning

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Machine learning operations with GitHub Actions and Kubernetes - GitHub Universe 2019

Watch on YouTube

#MachineLearning

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ARUBA: Learning-to-Learn with Less Regret

Link to the article

#LearningToLearn

🔭 @DeepGravity

This respository contains my exploration of the newly released TensorFlow 2.0. #TensorFlow team introduced a lot of new and useful changes in this release; automatic mixed precision training, flexible custom training, distributed GPU training, enhanced ops for the high-level #Keras API are some of my personal favorites. You can see all of the new changes here.

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The Ultimate guide to #AI, #DataScience & #MachineLearning, Articles, Cheatsheets and Tutorials ALL in one place

This is a carefully curated compendium of articles & tutorials covering all things AI, Data Science & Machine Learning for the beginner to advanced practitioner. I will be periodically updating this document with popular topics from time to time. My hope is that you find something of use and/or the content will generate ideas for you to pursue.

Link to the article

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#DeepFovea: #Neural Reconstruction for Foveated Rendering and Video Compression using Learned #Statistics of Natural Videos

Link to the paper

#FacebookAI

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Depth Predictions in #Art

Painters throughout art history have used various techniques to represent our three-dimensional world on a two-dimensional canvas. Using linear and atmospheric perspective, hard and soft edges, overlay of shapes, and nuanced hue and saturation, painters can render convincing illusions of depth on flat surfaces. These painted images, with varying degrees of “depth illusion" can also be interpreted by something entirely different: #MachineLearning models.

#DeepLearning

Link to the article

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