Innovation in battery technology has long lagged behind other advances, ColdFusion explores recent advances graphene battery technology and their long overdue arrival on the market.
Are you interested in learning Python?
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Every week, Analytics India Magazine interviews prominent data scientists to spread their success story with the community.
This week, they got in touch with Biswajit Biswas, chief data scientist at Tata Elxsi.
To stay competitive, Biswas spend at least 2 to 3 hours a day listening to podcasts, audiobooks, as well as reading books to be in touch with what is happening around. Nowadays, information is flooded across channels; which creates a lot of clutter. Hence, cutting out the noise and taking the correct gist has become a challenge.
One of the promising frontiers of research right now in chip design is using machine learning techniques to actually help with some of the tasks in the design process.
Here’s an interesting look at what Google is doing in this space.
We will be discussing this at our upcoming The Next AI Platform event in San Jose on March 10 with Elias Fallon, engineering director at Cadence Design Systems. (You can see the full agenda and register to attend at this link; we hope to see you there.) The use of machine learning in chip design was also one of the topics that Jeff Dean, a senior fellow in the Research Group at Google who has helped invent many of the hyperscaler’s key technologies, talked about in his keynote address at this week’s 2020 International Solid State Circuits Conference in San Francisco.
The need for on-device data analysis arises in cases where decisions based on data processing have to be made immediately.
For example, there may not be sufficient time for data to be transferred to back-end servers, or there is no connectivity at all.
Here’s a look at a few scenarios where this sort of localized compute will matter most.
Analyzing large amounts of data based on complex machine learning algorithms requires significant computational capabilities. Therefore, much processing of data takes place in on-premises data centers or cloud-based infrastructure. However, with the arrival of powerful, low-energy consumption Internet of Things devices, computations can now be executed on edge devices such as robots themselves. This has given rise to the era of deploying advanced machine learning methods such as convolutional neural networks, or CNNs, at the edges of the network for “edge-based” ML.
Check out how Stora Enso makes RFID tags green
Enabling automated item level processes and bringing business and environmental benefits to the industries the RFID (Radio Frequency Identification) based solutions are getting their lost interest back step by step.
In retail for example apparel industry and retailers are implementing RFID and driving related development. Material and manufacturing point of view Stora Enso has introduced most sustainable tags to the market – providing scalability and performance.
Python has been on a relentless ascent to distinction over the last few years and currently is one of the most well-known programming dialects on the planet.
Here’s an interesting article on why Python is so popular and what can be done with it.
Computer-based intelligence or Artificial Intelligence has made a universe of chances for application engineers. Computer-based information permits Spotify to prescribe artisans and melodies to clients, or Netflix to comprehend what shows you’ll need to see straight away. It is additionally utilized widely by organizations in client assistance to drive self-administration and improve work processes and worker efficiency.
Github shipped an updated version of good first issues feature which uses a combination of both a machine learning (ML) model that identifies easy issues, and a hand curated list of issues that have been labeled “easy” by project maintainers.
New and seasoned open source contributors can use this feature to find and tackle easy issues in a project.
In order to eliminate the challenging and tedious task of labelling and building a training set for a supervised ML model, Github has opted to use a weakly supervised model. The process starts by automatically inferring labels for hundreds of thousands of candidate samples from existing issues across Github repositories. Multiple criteria are used to filter out potentially negative training samples. These criteria include matching against a 300 odd curated list of labels, issues that were closed by a pull request submitted by a new contributor, and issues that were closed by pull requests that had tiny diffs in a single file.
PyTorch is definitely hot at the moment, especially with the recent 1.3 and 1.4 releases bringing a host of performance improvements and more developer-friendly support for mobile platforms.
Here are five reasons that add up to a strong case for PyTorch.
Due to the eager execution mode that PyTorch operates under, rather than the static execution graph of traditional TensorFlow (yes, TensorFlow 2.0 does offer eager execution, but it’s a touch clunky at times) it’s very easy to reason about your custom PyTorch classes, and you can dig into debugging with TensorBoard or standard Python techniques all the way from
print()statements to generating flame graphs from stack trace samples. This all adds up to a very friendly welcome to those coming into deep learning from other data science frameworks such as Pandas or Scikit-learn.