Synechron the global financial services consulting and technology services provider, has announced the launch of its AI Data Science Accelerators for Financial Services, Banking and Insurance (BFSI) firms. These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad.

Deep Learning and Machine Learning has made breakthroughs in recent years. There is tens of billions of dollars going into development of the new AI. Google and Deep Mind are recognizing that Deep Learning is not going to reach human cognition. They propose using models of networks to find relations between things to enable computers to generalize more broadly about the world. Deep learning faces challenges in complex language and scene understanding, reasoning about structured data, transferring learning beyond the training conditions, and learning from small amounts of experience.

More than half a million people are diagnosed with cancers of the head and neck each year, many of whom choose to undergo radiotherapy. But it's a delicate process: The surrounding tissue can be severely damaged if it isn't carefully isolated prior to treatments. In partnership with the University College London Hospital, Google subsidiary DeepMind is exploring ways artificial intelligence (AI) can aid in the segmentation process. It today announced a significant step forward in the pursuit of that vision: validation of a model that exhibits "near-human performance" on CT scans. "Automated … segmentation has the potential to address these challenges but, to date, performance of available solutions in clinical practice has proven inferior to that of expert human operators," the researchers wrote.

The question that I get the most from new and experienced machine learning engineers is "how can I get higher accuracy?" Makes a lot of sense since the most valuable part of machine learning for business is often its predictive capabilities. Improving the accuracy of prediction is an easy way to squeeze more value from existing systems. The guide will be broken up into four different sections with some strategies in each. Not all of these ideas will boost performance, and you will see limited returns the more of them you apply to the same problem.

Today's topic is … well, the same as the last one. Last time, we explained what Q Learning is and how to use the Bellman equation to find the Q-values and as a result the optimal policy. Later, we introduced Deep Q Networks and how instead of computing all the values of the Q-table, we let a Deep Neural Network learn to approximate them. Deep Q Networks take as input the state of the environment and output a Q value for each possible action. The maximum Q value determines, which action the agent will perform. The training of the agents uses as loss the TD Error, which is the difference between the maximum possible value for the next state and the current prediction of the Q-value (as the Bellman equation suggests).

Deep neural networks are a powerful method for automatically learning distributed representations at multiple levels of abstraction. Over the past decade, they have dramatically pushed forward the state-of-the-art in domains as diverse as vision, language understanding, robotics, game playing, graphics, health care, and genomics. The Deep Learning Summer School (DLSS) covers both the foundations and applications of deep neural networks, from fundamental concepts to cutting-edge research results.

We are excited to announce the release of the Deep Learning and Reinforcement Learning Summer School videos!

A few years ago I saw this headline news flashing all over the internet. Our dealers are missing up to $18 billion in easy sales. The Chairman and CEO of Caterpillar suggested that the company and its dealers were losing $9 - 18 billion in easy sales revenue as their sales, both internal and dealer networks, weren't monetising the real value of data. They are not tapping into the wealth of real-time customer data now at their fingertips; they are not communicating with each other; and they are not providing customers across the globe with a consistent experience when it comes to everything from e-commerce to parts and services pricing. Long story short, the whole idea was to convert the company's mentality from dumb iron sales to data-driven, machine learning-driven sales.

Having a solid grasp on deep learning techniques feels like acquiring a super power these days. From classifying images and translating languages to building a self-driving car, all these tasks are being driven by computers rather than manual human effort. Deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis. Understandably, a ton of folks are suddenly interested in getting into this field. But where should you start? What are the core concepts that actually make up this complex yet intriguing field? I'm excited to pen down a series of articles where I will break down the basic components that every deep learning enthusiast should know thoroughly. My inspiration comes from deeplearning.ai, In this article, I will be writing about Course 1 of the specialization, where the great Andrew Ng explains the basics of Neural Networks and how to implement them. Note: We will follow a bottom-up approach throughout this series – we will first understand the concept from the ground-up, and only then follow it's implementation.

I have heard that both feed-forward networks as well as recurrent neural networks are universal function approximators, i.e., they can approximate any function arbitrarily close with increasing hidden layers and hidden units. Is there a difference between smooth and non-smooth functions? What is better, a few hidden layers with many hidden units per layer or deep learning, i.e., many hidden layers? Has anyone tried to use LSTM networks to do this kind of stuff?