This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artifacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique…

Introduction

In a regular neural network, the input is transformed through a series of hidden layers having multiple neurons. Each neuron is connected to all the neurons in the previous and the following layers. This arrangement is called a fully connected layer and the last layer is the output layer. In Computer Vision applications where the input is an image, we use convolutional neural network…

Machine Learning / Deep Learning models can be used in different ways to do predictions. My preferred way is to deploy an analytic model directly into a stream processing application (like Kafka Streams or KSQL). You could e.g. use the …

1 Introduction

Credit risk or credit default indicates the probability of non-repayment of bank financial services that have been given to the customers. Credit risk has always been an extensively studied area in bank lending decisions. Credit risk plays a crucial role for banks and financial institutions, especially for commercial banks and it is always difficult to interpret and manage. Due to the advancements in technology, banks have managed to reduce the costs, in order to…

Each year, Risk Quant Europe Conference, a conference well-attended by practitioners from banking, asset management, insurers as well as academics from Europe, selects two papers to present in their annual conference.

For 2018, our paper is lucky to be one of the two winning papers selected by the Advisory Board for the conference to be held in London. Please feel free to check out our paper titled CDS Rate Construction Methods by Machine Learning…

Neural network or artificial neural network is one of the frequently used buzzwords in analytics these days. Neural network is a machine learning technique which enables a computer to learn from the observational data. Neural network in computing is inspired by the way biological nervous system process information.

Biological neural networks consist of…

Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.

For example, you may find a few papers discussing optimal selection of K in…

Cross Validation is often used as a tool for model selection across classifiers. As discussed in detail in the following paper https://ssrn.com/abstract=2967184, Cross Validation is typically performed in the following steps:

• Step 1: Divide the original sample into K sub samples; each subsample typically has equal sample size and is referred to as one fold, altogether,…

In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

• Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
• Support Vector Machine: e.g., the optimal choice of Kernel Functions
• Ensemble: e.g., the number of Learning Cycles for Bagging.
• Discriminant Analysis: e.g., Linear/Quadratic; regularization…

Past literature show that the comparisons of classifier's performance are specific to the types of datasets (e.g., Pharmaceutical industry data) used; i.e., some classifiers may perform better in some context than others. A paper titled CDS Rate Construction Methods by Machine Learning Techniques conducts the performance comparison exclusively in the context of financial market by applying a wide range of classifiers to provide solution to so-called Shortage of…

If you haven’t started using artificial intelligence in your business, you’re falling behind on the curve. Many business owners today are leveraging AI, whether they are aware of it or not. This is done through everyday business software suites that integrate machine learning and automation to carry out such functions as email communications, voice recognition and response and predictive analysis.

The extent to which businesses employ AI solutions needs to be increased if they are to…

Forbes magazine has been publishing the list of The World's Most Powerful People since 2009. The number of people in the list is proportional to the global population with the ratio being one slot for every 100 million people on Earth. When the list started in 2009, there were 67 people on the list and the latest list from year 2014 had 72 people. According for…

Buzz words are one of my least favorite things, but as buzz words go, I can appreciate the term “Data Lake.” It is one of the few buzz words that communicates a meaning very close to its intended definition. As you might imagine, with the advent of large scale data processing, there would be a need to name the location where lots of data resides, ergo, data lake. I personally prefer to call it a series of redundant commodity servers with Direct-Attached Storage, or hyperscale computing with…

On 19^th…