Deep Learning vs XGBoost, MXNet, Hugging Face & more...
This Week's top AI Stories
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We organising a virtual round table discussion on June 30, 2021, covering the theme: “Analytics as a Driver of Talent Transformation”, in association with Jigsaw Academy.
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1
Deep Learning vs XGBoost for Tabular Data
There have been several claims recently that deep learning models outperformed XGBoost. To verify this claim, a team at Intel published a survey on how well deep learning works for tabular data and if XGBoost superiority is justified.
The study showed XGBoost outperformed DL models across a wide range of datasets and the former required less tuning. However, the paper also suggested that an ensemble of the deep models and XGBoost performs better on these datasets than XGBoost alone.
2
bluCognition
US-based bluCognition, with the India HQ in Pune, is an AI/ML-based analytics startup that focuses on enhancing organisations’ analytical capabilities by leveraging alternative data sources and offering analytics consulting. Founded in 2017 by Sangarsh Nigam, the President & CEO, the management team boasts over two decades of analytics experience in leadership roles in the global payments industry.
We spoke with one of the key members of bluCognition’s management team — Mukesh Chamedia, the Managing Director, to learn more about its services and client partnerships.
3
Can MXNet Stand Up To TensorFlow & PyTorch?
Not many people are aware of MXNet, but it has emerged as one of the strong contenders to industry favourite frameworks such as TensorFlow and PyTorch. Notably, Amazon uses it for its deep learning web services.
MXNet stands for mix-net since it has been developed by combining several programming approaches into one. It supports languages such as Python, R, C++, Perl, and Julia. MXNet fits in small amounts of memory and ipso facto can be deployed to mobile devices or smaller systems.
So how does MXNet stack up against TensorFlow and PyTorch?
4
Featured Video | The journey of Hugging Face - Leading NLP Startup
5
Why Should A Robotics Researcher Care About A/B Testing?
A/B testing is a popular technique, a statistical endeavour, to find out the next best product or the most profitable website design and many more. So, what’s A/B testing got to do with robotics? Classical research methods such as A/B testing are not a default option in robotics research.
But, researchers said the methods are critical to producing meaningful and measurable scientific results for robotics in real-world scenarios.
6
Hands-on Guides for ML Developers
+ Guide To Infinite Nature For Perpetual View Generation
+ Image Blending Using Pyramids In OpenCV
+ What Is A Convolutional Layer?
+ Creating an Instance-Based Learning Model Using Weka
+ Guide to Feed-Forward Network using Pytorch with MNIST Dataset
7
PEOPLE & STARTUPS
+ India's New Geospatial Data Policy Is A Game Changer: Agendra Kumar, Esri
+ IBM Research Engineer Abhishek Bhandwaldar On His Machine Learning Journey
+ Clearwater Analytics’ Anurag Singh On India's SaaS Explosion
+ Interview With Dr Suraj Rengarajan, CTO, Applied Materials, India
+ Red Hat’s Neeraj Bhatia On How Open Source Defines The Future Of Technology
+ COO Manu Lavanya On Max Life Insurance’s Digital Journey
+ How Video Analysis Startup Netra Got Mark Cuban On Board With A Cold Email Pitch
+ How Space Startup Pixxel Uses AI To Monitor Earth
+ This Jaipur Startup Uses AI To Offer Customised Video Conferencing Solutions
8
Is India’s 67th Rank In Coursera Global Skills Report Justified?
Recently, Coursera released its annual Global Skills Report 2021 to offer a sneak peek into the ‘state of skills’ in over 100 countries. India, as per the report, ranks 67th globally with 38% proficiency and 16th in Asia below Singapore and Japan. The country stood at 55th position in business and at 66th position in both technology and data science.
The rankings attributed to India in data sciences seems inaccurate and reflects a “one size fits all” methodology.
9
How A Retro Video Game Ended Up As An Ultimate Challenge For AI
Games make for brilliant testbeds for reinforcement learning—and this has been done with machine learning models for quite some time now. For instance, through DQN models, AI outperformed humans while playing games such as the once widely popular Flappy Bird. Such AI testing has also been performed on classics such as chess and the mobile Snake game.
Given AI’s prowess in cracking games through reinforcement learning, researchers at Facebook’s AI wing have decided to leverage AI to unravel what is considered among the world’s most demanding games: the immensely complicated NetHack.
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BOTTOM OF THE NEWS
Here's what all happened last week.
+ Andrew Ng Launches Data-Centric AI Competition
+ Nasscom Rubbishes Report Projecting 3Mn Job Losses In IT Sector
+ Hugging Face Launches Free NLP Course
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