APART from highlighting the digital vulnerabilities in tertiary education institutions, the revelation of cyber attacks on four main Singaporean universities last week has caught the attention of cyber security experts. One of them is Stuart Fisher, the Senior Vice President of Deep Instinct, a cybersecurity startup that sees itself revolutionising the cybersecurity industry through the application of deep learning. To recap, the Cyber Security Agency of Singapore (CSA) and the country's Ministry of Education (MOE) said last week that they received information about the breaches affecting at least 52 online accounts. The incident was found to be a phishing attack where unsuspecting users were directed to a credential harvesting website. Credentials were then used to gain unauthorized access to the institutes' library website to obtain research articles published by staff.

Many machine learning and artificial intelligence (AI) systems lack the ability to explain how they work and make decisions--and this is a major trust inhibitor. They can find patterns in data that elude us, patterns that might reveal important relationships that improve the accuracy of the algorithm. They can recover patterns and relationships that we as human beings want to ignore. But they can just as easily fail to discover important relationships and produce bad recommendations, even dangerous ones. A well-known example of the latter involved research to see whether machine learning could guide the treatment of pneumonia patients.

In my earlier article, we talked about the usage of machine learning algorithms in autonomous cars. Obviously, the process of learning and implementation of machine learning is not without a huge set of challenges. Attaining superior accuracy of detection and prediction: Safety-critical systems as used in self-driving cars, require detection accuracy much higher than in the internet industry. These systems are expected to operate flawlessly irrespective of weather conditions, visibility, or road surface quality. Challenge of scale: deep neural networks, such as those used in self-driving vehicles, require a mind-boggling amount of computational power.

In every real world problem, the first step to build a solution focused model is to perform an exploratory data analysis. This will estabilish the suitable model for the problem, which can be further used to tune up the performance and solve the problem efficiently. Exploratory data analysis for artificial neural network deals in playing with the hidden layers and activation functions. Advanced big-data problems, image based problems and many other complex problems arre now tackled with Convolution Neural Networks (CNN). Deep learning has been extensively used in many complex research problems, because of it's ability to gain insights from big data, skip the process of data feature extraction in many cases (CNN can work directly on the images, without any feature extraction).

Keras is a high-level neural network library written in Python, and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner. Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing.

Big data could be the future of the new antibiotic in the health care industry to enhance the patient medical care. Big data is shifting the paradigm by creating a new data-driven health care industry perfecting the precision on performing the surgery based on the data points built by big data analytics. Big data analytics tools are aiding corporations in developing new medicine every day with data science more accurately. According to a modern medical research estimate, the pharmaceutical product cost from the lab to the consumer is around $2.6 billion dollars. McKinsey, research estimated big data analytics tools would lower the health care industry costs to the tune of $493 billion dollars.

"If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown," says Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications (NCSA), and the principal investigator of the new study. The advancement, published in Remote Sensing of Environment, is a breakthrough because, previously, national corn and soybean acreages were only made available to the public four to six months after harvest by the USDA. The lag meant policy decisions were based on stale data. But the new technique can distinguish the two major crops with 95 percent accuracy by the end of July for each field -- just two or three months after planting and well before harvest. The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more.

As part of this expansion, IBM added more data scientists and AI engineers, which has resulted in new projects, such as the Model Asset eXchange (MAX) and the Fabric for Deep Learning (FfDL) which is pronounced "fiddle." MAX is an open source ecosystem for data scientists and AI developers to share and consume models that use machine learning engines, such as TensorFlow, PyTorch and Caffe2, Diaz said. It also provides a standard approach to classify, annotate, and deploy these models for prediction and inferencing. Additionally, developers can train and deploy MAX models for production workloads that use Watson Studio, such as internet-of-things applications, said Guido Jouret, chief digital officer at ABB. IBM's MAX not only avoids the cost and time for developers to create these models themselves, but they also get access to the open source community to continually add and improve on these models, said Kathleen Walch, senior analyst at Cognilytica, based in Washington, D.C.. "It helps level the playing field for smaller companies [that] don't have as much data or resources," she said. Meanwhile, FfDL presents a cloud-native service for popular open source frameworks TensorFlow, Caffe and PyTorch.

This video course presents deep learning architectures coded in Python using Keras, a modular neural network library that runs on top of either Google's TensorFlow or Lisa Lab's Theano backends. This video course introduces Generative Adversarial Networks (GANs) that are used to reproduce synthetic data that looks like data generated by humans, and then teach how to forge the MNIST and CIFAR-10 dataset with the help of Keras Adversarial GANs. Practical applications include code for predicting the surrounding words given the current word, sentiment analysis, and synthetic generation of texts. We will learn about a specific form of word embedding word2vec. This embedding has proven more effective and has been widely adopted in the deep learning and NLP communities.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you'll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you'll learn about Deep learning and TensorFlow.