TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop. Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks.

A Complete Guide on TensorFlow 2.0 using Keras API, Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0 Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Luka AnicinPreview this Course - GET COUPON CODE Welcome to Tensorflow 2.0! TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop. Deep Learning is one of the fastest growing areas of Artificial Intelligence.

As writers, we often seek out tools to help us become more efficient or productive. Tools such as Grammarly can help with language editing. Text generation tools can help to rapidly generate original contents by just giving the AI a few keyword ideas to work with. Perhaps this could help end writer's block? This is a debatable question that is best saved for a later time.

The term artificial intelligence is now applied to such a wide range of applications these days, it's difficult to know what AI truly is. We hear that Google is using AI in search, Facebook uses it for facial recognition and Netflix is using AI to'conquer the world'. These examples are all very interesting, but they do leave many wondering what exactly AI is and how can they apply it, now, to their everyday marketing tasks? To help marketers understand AI and how it applies to our craft, Econsultancy recently held a Digital Outlook event in Singapore and invited marketing AI expert Deborah Kay, Founder of Digital Discovery, to give an overview of the state of the art. Helpfully, Ms. Kay provided a summary of the four most exciting areas of AI for marketing as well as many examples of how AI is being used in the real world.

Last week, 5G Americas, the wireless trade group, announced the publication of a new whitepaper that focuses on the role 5G will play in wireless vehicle-to-everything communications. The self-driving or autonomous-driving revolution has been coming on strong over the last few years, but this next generation of wireless technology could be the real starting line. The 5G Americas whitepaper is called, "Cellular V2X Communications Towards 5G". If you've been paying attention, we have already entered this space. Driving many new cars, you will notice how they automatically update their operating systems, navigation, radio and dashboard controls while driving.

A few years ago, we could barely generate written digits. Then, the convolutional network came along and suddenly images became hundreds of times easier to deal with. In recent years, the generative adversarial network has brought the most magic with it. We can now generate celebrity faces at almost perfect accuracy, which in my opinion is amazing and something I had not even thought was possible. Also, something that's caught my attention is CycleGAN, which is a way to translate images (for example, cows- horses or daytime- nighttime), WITHOUT the need for training pairs!

Back in December, I wrote that one of the best sectors to invest in this year is artificial intelligence (AI). And that has proven to be the case so far. You don't even have to look at the stock market to see the rising importance of this emerging technology. Every dinner party I go to... every barbecue I attend... every birthday party or chat over coffee, the subject ultimately turns to AI. We're even inundated now with TV commercials about AI. You've probably seen an ad for IBM's (NYSE: IBM) Watson or Intel's (Nasdaq: INTC) "The Future of Artificial Intelligence" with The Big Bang Theory's Jim Parsons.

The blending of home-based diagnostic platforms with medical care at home is arriving. The Tricorder XPRIZE competition is well underway, with several teams set to compete in the final stages. Leading contenders include CloudDx and Scanadu, a company started at our first Exponential Medicine program, have successfully leveraged crowdfunding to enable their clinical trials. Gale by 19Labs is a next generation "first aid kit meets home health center" (see the below video for a demo) exemplifying how integration of home diagnostics paired with menu-driven (and potentially AI-driven) assistance and optional telemedicine connectivity can provide increased access to home-based diagnosis, triage and management of minor bumps and scrapes and also more complex medical conditions. Interactive and engaging, from coaching on diet and nutrition to reminding you to take your medications or offering psychological support and follow up -- the chatbots are on their way.

The blending of home-based diagnostic platforms with medical care at home is arriving. The Tricorder XPRIZE competition is well underway, with several teams set to compete in the final stages. Leading contenders include CloudDx and Scanadu, a company started at our first Exponential Medicine program, have successfully leveraged crowdfunding to enable their clinical trials. Gale by 19Labs is a next generation "first aid kit meets home health center" (see the below video for a demo) exemplifying how integration of home diagnostics paired with menu-driven (and potentially AI-driven) assistance and optional telemedicine connectivity can provide increased access to home-based diagnosis, triage and management of minor bumps and scrapes and also more complex medical conditions. Interactive and engaging, from coaching on diet and nutrition to reminding you to take your medications or offering psychological support and follow up -- the chatbots are on their way.

I am highlighting several recent papers that show the potential of residual neural networks. Residual neural networks, or ResNets (Deep Residual Learning for Image Recognition), are a technique Microsoft introduced in 2015. The ResNet technique allows deeper neural networks to be effectively trained. ResNets won the ImageNet competition in December with a 3.57% error score. Recently, researchers have published several papers augmenting the ResNet model with some interesting improvements.