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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Using Machine Learning to Help Protect the Great Barrier Reef in Partnership with Australia’s CSIRO by Megha Malpani and Ard Oerlemans via @TensorFlow

In spite of the costs, machine learning has been successfully used in a variety of conservation projects around the world. Here's an inside look at how the Great Barrier Reef Foundation leveraged the latest technologies to survey, monitor and map reefs at scale.

This is a great example of something good to come out of machine learning which generally gets so much bad press about it taking over jobs.

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.

Federated Learning: An Introduction by @_mwitiderrick via @fritzlabs

Improving machine learning models and making them more secure by training on decentralized data.

This is really interesting and I think gives us a clear explanation that we should all be able to follow.

WEBINAR: Forecasting Using TensorFlow and FB's Prophet - 17 October 2019

Data Science Central Webinar Series Event

Forecasting Using TensorFlow and FB's Prophet Join us for this latest DSC Webinar on October 17th, 2019 We live in a time where we are able to monitor everything--servers, containers, fitness levels, power consumption, etc. Making predictions on time series data is often just as important as monitoring is. In this presentation, we will learn about how InfluxDB can be used with TensorFlow and FB's Prophet to make predictions and solve data engineering problems. Speaker: Anais Dotis-Georgiou, Developer Advocate -- InfluxData Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Forecasting Using TensorFlow and FB's Prophet Date:  Thursday, October 17th, 2019 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

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.

TensorFlow ML framework for graphical data released by @TensorFlow via @Medium

TensorFlow released Neural Structured Learning (NSL), an open-source framework that uses the neural graph learning method for training neural networks with graphs and structured data.

This is a great article and has some code to make sense and so use your Medium account and give them some applause and a follow.

Understand TensorFlow by mimicking its API from scratch by @elmd_ via @Medium

This great tutorial mimics TensorFlow’s API and implements the core building blocks from scratch, giving you an under-the-hood look at how TensorFlow’s deep learning libraries work.

I love this - it is very clear and easy to understand. You really need to bookmark this if you want to understand or learn about TensorFlow.

Convolutional Neural Net in Tensorflow by Stephen Barter via @Medium

Here's a look at the fundamentals of convolutional neural nets and how you can create one yourself to classify handwritten digits.

This is a great guide and I think it is well worth a subscription to see what else the author has written on Medium - so much in this article to learn from.

What If.you could inspect a machine learning model, with no coding required? by/via @GoogleAI

Building effective machine learning systems means asking a lot of questions. It's not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better.

Kudos to them - they really are doing great things - I can only hope that one day I could be good enough to join them.

Convolutional neural networks for language tasks by Garrett Hoffman via @OReillyMedia

Though they are typically applied to vision problems, convolution neural networks can be very effective for some language tasks.

This looks really useful and it has snippets of code for you too.

The Google Brain Team - Looking Back on 2017 by Jeff Dean via @googleresearch

Jeff Dean from the Google Brain Team highlights his team's accomplishments for 2017. This is an amazing assortment of projects that have wide-ranging impact. There are two parts to this post and both are high-level with lots of screenshots, videos, and links.

Part 1

Part 2

Wow - what a huge list of things that they have achieved - I can't wait to see what they do next.

Object detection with TensorFlow by Justin Francis via @oreillymedia

How to create your own custom object detection model. (Includes Python code and iPython notebook.)

This is great and you can access the code on Github.

TensorFlow: Building Feed-Forward Neural Networks Step-by-Step by Ahmed Gad via @kdnuggets

This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.

Please note this is a 3 page article.  I love that this is so clear and I think easy to understand.

Keras Cheat Sheet: Deep Learning in Python by Karlijn Willems via @DataCamp

Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models. With this library, you’ll be making neural network models in no time!

This is great - well worth signing up with DataCamp and getting some tools and courses that are incredibly useful.

WEBINAR: Human-in-the-Loop Deep Learning - 28 September 2017

Overview

Title: Human-in-the-Loop Deep Learning

Date: Thursday, September 28, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Human-in-the-Loop Deep Learning

AI systems need to continually learn from new data to perform well in real-world scenarios. However, it is non-trivial to decide what new data needs to be labelled for training, and what is the best workflow and user interface for providing human feedback. This critical component of Machine Learning, called Active Learning, is often absent from Machine Learning courses. This Data Science Central webinar will extend TensorFlow's Deep Learning functionality with several Active Learning strategies, and apply these to the well-known ImageNet Computer Vision data set. At the end of this webinar you should be comfortable with combining your data annotation and Machine Learning strategies to continually improve your training data at scale.

Speaker: Robert Munro, VP of Machine Learning  -- CrowdFlower 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

 

Register here

The ultimate promise of artificial intelligence lies in sorting cucumbers by Dave Gershgorn via @qz

It can take months to learn how to properly sort spiky cucumbers. "When I saw Google's AlphaGo," explains Japanese farmer Makoto Koike, "that was the trigger for me to start developing the cucumber sorter with deep learning technology."

A great example of a practical use for AI.

Google Offers Free Software in Bid to Gain an Edge in Machine Learning via @nytimesbits

Google is making much of its machine-learning technology (TensorFlow) freely available as open-source software. This sounds great on the face of it, but it hinges on several factors:

• How will it be maintained?
• Who will maintain it?
• How good actually is it?

I look forward to the answers to those questions.