This is a directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library. If you have a high-quality tutorial or project to add, please open a PR.

BEGIN ARTICLE PREVIEW: DeepMind has created an artificial intelligence system that can rapidly and accurately predict how proteins fold to get their 3D shapes. END ARTICLE PREVIEW READ MORE FROM SOURCE ARTICLE

While some of the applications for artificial intelligence involve say, winning games of Texas hold'em or recreating pretty paintings, there are areas where the technology could have truly profound consequences. Among those is medical care, and a major breakthrough from Alphabet's DeepMind AI could be a gamechanger in this regard, with the system demonstrating an ability to predict the 3D structures of unique proteins, overcoming a problem that has plagued biologists for half a century. By understanding the 3D shapes of different proteins, scientists can better understand what they do and how the cause diseases, which in turn paves the way for better drug discovery. Beyond that, as a central component to the chemical processes for all living things, more expedient mapping of 3D protein structures would benefit many fields of biological research, but this process has proven painstaking. This is because while modern scientific tools such as X-ray crystallography and cryo-electron microscopy allow researchers to study these structures in amazing new detail, they all still hinge on a process of trial and error.

At the height of the exchange of accusations between the United States and China regarding the "Covid-19" disease, new signs of a war between the two countries appeared, the Artificial intelligence War, which lead us to ask: Is this technology ready to work in safety? And can military AI be deceived easily? Although military AI technologies dominate military strategy in the US and China; But what sparked the crisis was that last March, Chinese researchers launched a brilliant, and potentially devastating, attack against one of America's most valuable technological assets, the Tesla electric car. A research team from the security laboratory of the Chinese technology giant "Tencent" has succeeded in finding several ways to deceive the artificial intelligence algorithms in the Tesla electric car by carefully changing the data, which are fed to the car's sensors, and the team managed to trick and confuse the vehicle's AI. The team tricked Tesla's brilliant algorithms capable of detecting raindrops on the windshield or following the lines on the road, operating the windshield wipers to act as if there was rain, and the lane markings on the road were modified to confuse the autonomous driving system so that it passed in the opposite traffic lane in violation of traffic rules.

Deep learning algorithms take millions of data points as inputs, correlating specific features to produce an output. While humans are involved in the initial management of data, such as data labeling, once fed into a system the process is largely self-directed. Even for the data scientists and programs involved in the model's development, it can be difficult to interpret and subsequently explain how a process has led to a specific output. This is a complex issue earning the label'black box AI' -- and it is becoming a greater problem as artificial intelligence (AI) and machine learning plays a bigger role in our day-to-day and working lives. When the workings of software used for important operations and processes within a business cannot be easily viewed or understood, errors and bias can go unnoticed and snowball into much bigger, potentially irreparable, problems.

I'm excited to introduce The DL-- a Medium publication with a focus on deep learning. Deep learning has absolutely exploded in recent years. Seemingly every tech company/startup boasts a killer AI feature, and ML-related skills are in extremely high demand. Deep learning research is also evolving very quickly, and new groundbreaking research is published almost every week. This makes it difficult for practitioners to stay up-to-date on the latest algorithms, frameworks, and best practices.

Researchers discussed how they used a deep-learning algorithm to detect, quantify, and assess the severity of infarcts in the brain on diffusion-weighted MRI (DWI-MRI) exams in acute ischemic stroke patients in a Sunday presentation at the virtual RSNA 2020 meeting. A team of researchers led by presenter Seung Hyun Hwang of Yonsei University in Seoul, South Korea, developed a deep-learning model that can segment and quantify brain infarcts using DWI-MRI and then assess their severity by analyzing apparent diffusion coefficient (ADC) maps of the lesions. In testing, the model achieved high sensitivity and specificity. "The qualitative and quantitative results of our study show feasibility for detecting and quantifying infarcts," Hwang said. Due to its sensitivity for the detection of small and early infarcts, DWI-MRI is commonly used for evaluation of acute ischemic stroke, according to Hwang.

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in Python using Keras and Tensorflow libraries and analyze their results. Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

Artificial Intelligence or popularly known as AI, has been the main driver of bringing disruption to today's tech world. While its applications like machine learning, neural network, deep learning have already earned huge recognition with their wide-ranging applications and use cases, AI is still in a nascent stage. This means, new developments are simultaneously taking place in this discipline, which can soon transform the AI industry and lead to new possibilities. So, some of the AI technologies today may become obsolete in the next ten years, and others may pave the way to even better versions of themselves. Let us have a look at some of the promising AI technologies of tomorrow.

For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it's not an easy problem to solve. Google's Deepmind claims to have created an artificially intelligent program called "AlphaFold" that is able to solve those problems in a matter of days. The latest version of DeepMind's AlphaFold, a deep-learning system that can accurately predict the structure of proteins to within the width of an atom, has cracked one of biology's grand challenges. According to John Moult, Professor at the University of Maryland, "It's the first use of AI to solve a serious problem,". In the experiment, DeepMind used a new deep learning architecture for AlphaFold that was able to interpret and compute the'spatial graph' of 3D proteins, predicting the molecular structure underpinning their folded configuration.