With the adoption of IoT, connected applications and systems are moving to the cloud.

The number of end-devices and data generated on the cloud is also increasing. Edge devices like sensors, mobile devices, wearables, robots, and many other connected devices in IoT ecosystem generate a huge amount of decentralized data.

Due to lack of reliable connectivity, delays and difficulties in processing this huge data on cloud, there is a challenge in analyzing and extracting important insights from this data. To deal with this challenge, enterprises are leveraging edge analytics along with cloud computing.

This combination brings stability in the IoT network by bringing the computational power near to the source of data and reducing the delays in analytics, resulting in real-time insights and resolutions for the problems of various industries. In other terms, when data cannot be taken to the algorithm, edge analytics brings algorithms to the data and provide important insights.

Microsoft has announced the availability of GPU Compute on the WSL 2.

WSL allows users to run native Linux command-line tools directly on Windows 10. Short for Windows Subsystem for Linux, WSL runs on over 3.5 million monthly active devices.

According to Microsoft, GPU Compute on WSL has always been the most-requested feature.

The preview of GPU Compute for WSL2 will initially be compatible with Artificial Intelligence (AI) and Machine Learning (ML) workflows. Plus, it will allow WSL users to operate Machine Learning training workloads directly on Windows. At this year’s Build 2020 developer conference, Microsoft revealed its plans to make the preview of GPU Compute available within WSL for Windows Insiders.

freeCodeCamp.org posted this DeepLizard video.

This course will teach you how to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!

Course index:

  • (00:00:00) Welcome to this course
  • (00:00:16) Keras Course Introduction
  • (00:00:50) Course Prerequisites
  • (00:01:33) DEEPLIZARD Deep Learning Path
  • (00:01:45) Course Resources
  • (00:02:30) About Keras
  • (00:06:41) Keras with TensorFlow – Data Processing for Neural Network Training
  • (00:18:39) Create an Artificial Neural Network with TensorFlow’s Keras API
  • (00:24:36) Train an Artificial Neural Network with TensorFlow’s Keras API
  • (00:30:07) Build a Validation Set With TensorFlow’s Keras API
  • (00:39:28) Neural Network Predictions with TensorFlow’s Keras API
  • (00:47:48) Create a Confusion Matrix for Neural Network Predictions
  • (00:52:29) Save and Load a Model with TensorFlow’s Keras API
  • (01:01:25) Image Preparation for CNNs with TensorFlow’s Keras API
  • (01:19:22) Build and Train a CNN with TensorFlow’s Keras API
  • (01:28:42) CNN Predictions with TensorFlow’s Keras API
  • (01:37:05) Build a Fine-Tuned Neural Network with TensorFlow’s Keras API
  • (01:48:19) Train a Fine-Tuned Neural Network with TensorFlow’s Keras API
  • (01:52:39) Predict with a Fine-Tuned Neural Network with TensorFlow’s Keras API
  • (01:57:50) MobileNet Image Classification with TensorFlow’s Keras API
  • (02:11:18) Process Images for Fine-Tuned MobileNet with TensorFlow’s Keras API
  • (02:24:24) Fine-Tuning MobileNet on Custom Data Set with TensorFlow’s Keras API
  • (02:38:59) Data Augmentation with TensorFlow’ Keras API
  • (02:47:24) Collective Intelligence and the DEEPLIZARD HIVEMIND

Microsoft Mechanics learns how UK-based data engineering consultant, endjin, is evaluating Azure Synapse for on-demand serverless compute and querying.

Endjin specializes in big data analytics solutions for customers across a range of different industries such as ocean research, financial services, and retail industries.

Host Jeremy Chapman speaks with Jess Panni, Principal and Data Architect at endjin, to discuss how they’re using SQL serverless for on-demand compute as well as visualization capabilities to help customers with big data challenges. If you are new to Azure Synapse, it’s Microsoft’s limitless analytics platform that brings enterprise data warehousing and big data processing together into a single service, removing the traditional constraints for analyzing data of all shapes and sizes.

For more information on endjin and how they help small teams achieve big things, check out their website at https://endjin.com

Watch an introduction to Azure Synapse at https://aka.ms/mechanicssynapse 

Check out other early adopters on our How We Built It series at https://aka.ms/AzureSynapseSeries

My Headshot

The following is a guest post by Amanda Jerelyn. She is currently working as an IT Manager at Dissertation Assistance. She is a working-from-home single mother who has significant experience of working with AI from her previous job. Now she spends most of her time blogging about her experiences and sharing them with a like-minded audience.

As the new decade started with some turbulence amongst the world’s nuclear powers, people suspected it would not be a smooth road to the end of 2020. Fast forward four months into the year, we found ourselves having anxiety attacks in our homes, unable to go out, stuck in a global pandemic, and suggested to quarantine.

The new novel coronavirus named SARS-CoV-2 struck the world in late March and went on to infect more than 735,000 patients worldwide. According to the statistical update posted by the World Health Organization, the virus has caused more than 34,000 deaths globally.

Now as of June 2020, considering these facts, the year has been one of the worst ever since the great depression and the World War era. The only difference is that we are far more technologically advanced than ever to tackle this situation at hand. As COVID-19 dragged many economies down with it, the need to answer the grey areas related to illness has increased. Therefore, as the results from researches pour in, in addition to human intelligence, there is an excellent scope for artificial intelligence.

7 Ways AI is Helping Fight COVID-19

Here we have listed seven ways AI is being used to help fight COVID-19.

1. Identifying vulnerable patients

The research study to identify vulnerable patients through AI shows a promising future. The goal behind the study is to design and make use of a decision-making tool using an AI-powered skill set like predictive analytics. It will help mark the future estimation of the severity of the virus spreading through vulnerable patients. Knowing that patients with underlying diseases are more at risk to catch this disease has helped in spreading awareness to take extra care.

2. Predicting statistics

According to the statistics published in the Chinese research journal named Computers, Materials & Continua, the future severity of the virus can be more worrying given the fact that doctors and physicians don’t deploy the proper tools. As per the data collected for the study from 53 COVID-19 positive patients at two leading Chinese hospitals, symptoms developed over a week. Keeping China as the main focus of the research and considering demographics, laboratory, and radiological results, the researchers went to predict which of the patients over the period developed pneumonia and ARDS that eventually led to being fatal to the patients.

3. Surveillance of the disease

Considering how infectious and contagious of a disease the new novel coronavirus, keeping it under observation is crucial. It can be confidently said that traveling, tours, and migration from place to place has been the core reason this disease spread worldwide in just a matter of days. Thus it was soon significantly evident that keeping track of how human activity transmits this disease is essential to control the progression. In the near future, it is highly likely that AI will not only predict its spread but also keep it under surveillance to notice any changes and control the spread.

4. Keeping up with the demographics

There is no doubt that artificial intelligence is best suited for collecting relevant data and making critical analysis. Therefore, in this case of the virus at our hands, it is vital to use AI in domains where it has already worked. A lot with the virus spreading has to do with the associated details of the patients that have been attacked and of those who show no symptoms even after testing positive for the disease. It is beyond human intelligence to make such specific calls and base decisions upon them. Thus, AI can be of huge help to keep up with such demographics.

5. Better information handling

AI can help gather, categorize, and utilize essential data and information based on hospitals, staff, doctors, and medical equipment. Many countries around the world that have been topping the charts of the disease with the most number of cases that don’t have the appropriate amount of medical equipment and labor resources to cope with the magnitude of the disease spreading. Therefore, AI technology must keep track of such vital information to help notify the authorities and officials responsible for dealing with the situation at the earliest time before it’s too late.

6. Virtual healthcare assistance for the quarantined

Artificial intelligence can be incredible for providing virtual assistance along with HND assignment help to all doctors, patients, and even the general public for providing answers to queries in the comfort of their own homes. As the government and officials recommend that it is in the best benefit of all to stay home, technology like AI can be of great interest in contributing to the cause. Chatbots can be programmed to answer FAQs of the general public, provide reminders to check fever in patients for doctors and nurses, and then link up to actual doctors and experts in a critical situation.

7. Diagnostic AI

As per how it has been observed, COVID-19 requires immediate diagnosis in a critical situation to differentiate it from being common cold, flu, and pneumonia. AI-powered technology can help do that immediately, whereas human intelligence may take up more time and resources to do so as these diseases show up very similar symptoms. AI has dramatically improved diagnostic time throughout the COVID-19 crises as researchers continued developing the technology and making it better for future diagnoses. It still is a developing procedure, but as diagnostic AI lends a hand to other medical equipment like CT Scans and MRIs, the results are profitable.

Bottom Line

All in all, as the researches continue to be conducted ever so diligently, it can be said that researchers and experts are desperate to find a solution to put an end to this unprecedented situation. Until then, COVID-19 has become a stark reality we will have to learn to live with.

Author Bio: Amanda Jerelyn is currently working as an IT Manager at Dissertation Assistance. She is a working-from-home single mother who has significant experience of working with AI from her previous job. Now she spends most of her time blogging about her experiences and sharing them with a like-minded audience.