ONLINE CONFERENCE - oneAPI DevSummit for AI 2022 - 12 July 2022

 

Are you a researcher, data scientist, or developer looking to build AI applications and seamlessly scale them from edge to cloud?

Join us for a day of discovery with renowned industry experts who will demystify the latest technologies, tools, trends, and techniques.

• Drop-in optimizations across popular frameworks and libraries for deep learning, machine learning, and data analytics—TensorFlow, PyTorch, scikit-learn, and more.
• Intel AI tools for end-to-end development—data preparation, training, inference, deployment, and scaling
• A hands-on workshop on dinosaur hunting (yes, you read that correctly)
• Opportunities to attend tech talks and panel discussions with tech experts from Google, Accenture, RedHat, JD.COM, Aible and more.

Expand your view and vision across the AI technology spectrum to get started on your development journey or supercharge your existing one.

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Write Better And Faster Python Using Einstein Notation by Bilal Himite via @TDataScience

Make your code more readable, concise, and efficient using “einsum”

I had never heard of this and was fascinated to find out more. I also found this additional article useful:

Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch

Top 7 Data Libraries You Will Absolutely Need for Your Next Deep Learning Project @orhangaziyalcin via @TDataScience

You might be an expert in TensorFlow or PyTorch, but you must take advantage of these open-source Python libraries to succeed.

A very useful list of some of the most relevant libraries to use if you want to do a Deep Learning project. This could save you the time you would have spent searching for the right libraries.

Autograd: The Best Machine Learning Library You’re Not Using? by Kevin Vu via @Exxactcorp

 If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.

This is great and contains code fragments in order to help you explore this with a view to using it.

The Most Important Fundamentals of PyTorch you Should Know by Kevin Vu via @Exxactcorp

PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.

This was incredibly clear and very useful as it contained code examples for you to learn from. Definitely recommended.

Lossless Image Compression through Super-Resolution by Sheng "Scott" Cao via @github

This is the official implementation of SReC in PyTorch. SReC frames lossless compression as a super-resolution problem and applies neural networks to compress images. SReC can achieve state-of-the-art compression rates on large datasets with practical runtimes. Training, compression, and decompression are fully supported and open-sourced.

This is really interesting and very useful. The link is to a paper in Github.

The Most Useful ML Tools 2020 by @ian_xxiao via @TDataScience

5 sets of tools every lazy full-stack data scientist should use

This is a great list of tools and should give you a list of tools to try. Give Ian some applause on Medium and a follow.

Interested in machine learning? Better learn PyTorch by Matt Asay via @infoworld

Don’t look now, but easy, straightforward PyTorch has become the hottest product in data science.

As it rivals Tensorflow I would probably suggest you get a grounding in both (if you can).

Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch by @jrdothoughts via @TDataScience

The new release of PyTorch includes some impressive open-source projects for deep learning researchers and developers.

Interesting new features that definitely call for some experimenting to see what they can really do.

Which Data Science Skills are core and which are hot/emerging ones? by Gregory Piatetsky, via @kdnuggets

They have identified two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

This should be very useful for anyone who is already working in or wants to be working in Data Science. Great diagrams too.