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… Continue
Added by Sanjiban Sekhar Roy on March 1, 2019 at 11:30pm —
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… Continue
Added by Muhammad Rizwan on September 24, 2018 at 3:00pm —
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 … Continue
Added by Kai Waehner on July 8, 2018 at 4:26pm —
Having my newsfeed cluttered with articles about Google creating an AI that beats hospitals by predicting death with 95% accuracy (or some other erroneous claim), I dug up the original research paper to fact check this wondrous new advancement. Many of said articles used this quote from the abstract (academia's equivalent of a paperback blurb):
Added by Stephen Chen on July 4, 2018 at 1:30am —
The mantra for technology evolution has been to replace or minimize human assistance with machines. Traditionally human support systems are rapidly being replaced by machines & by automation. The dependence on human decision-making is shrinking fast. If we take a domestic cooking gas stove as an example, we now have the automated safety gas stove where the gas supply can be cut off completely by itself in case of gas leakage or mishandling, thus avoiding fatal accidents and… Continue
Added by Yuvan Asav on March 27, 2018 at 10:00pm —
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… Continue
Added by Shruti Goyal on March 14, 2018 at 11:30am —
Data classification is the central data-mining technique used for sorting data, understanding of data and for performing outcome predictions. In this small blog we will use a library Smilecthat includes many methods for supervising and non-supervising data classification…
Added by jwork.ORG on March 13, 2018 at 4:00pm —
This research work has been carried out jointly by Deepak Kumar Gupta & Shruti Goyal November 11, 2017
In the real world, many online shopping websites or service provider have single email-id where customers can send their… Continue
Added by Deepak Kumar Gupta on March 10, 2018 at 1:30pm —
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… Continue
Added by Zhongmin Luo on February 24, 2018 at 2:00am —
In this series, I will talk about training a simple neural network on image data. To give a brief overview, neural networks is a kind of supervised learning. By this I mean, the model needs to train on historical data to understand the relationship between input variables and target variables. Once trained, the model can be used to predict target variable on new input data. In the previous posts, we have written about linear, lasso and ridge regression. All those methods come under… Continue
Added by Jobil Louis on January 16, 2018 at 8:00pm —
Following the original observations of Neural Networks in action; I decided a follow up was needed. In the original blog, ; the smallest neural net (NN) that learnt the data set was 2-6-3-1 but the details were not saved, a second NN with the same configuration came close but its approach was different. The… Continue
Added by Sierra Oscar on January 4, 2018 at 10:09am —
This post is designed show the internal changes of an Artificial Neural Network (ANN/NN), it shows the outputs of the neurons from the beginning of a Backpropagation algorithm to convergence.
The hope is for a better understanding of why we use a second hidden layer, local minimums, to how many internal nodes are required and their impact on the final solution.
The Dataset… Continue
Added by Sierra Oscar on November 15, 2017 at 9:33pm —
What’s the first thing that comes to mind when you hear the following phrases?
- Artificial grass
- Artificial sweeteners
- Artificial flavors
- Artificial plants
- Artificial flowers
- Artificial diamonds and jewelry
- Artificial (fake) news
These phrases probably evoke thoughts such as “fake,” “not real,” or even “shabby.” Artificial is such a harsh adjective.…
Added by Bill Schmarzo on October 30, 2017 at 6:30pm —
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… Continue
Added by Ashish Sukhadeve on August 6, 2017 at 7:00am —
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… Continue
Added by Zhongmin Luo on June 5, 2017 at 7:30pm —
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,…
Added by Zhongmin Luo on June 2, 2017 at 7:00pm —
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…
Added by Zhongmin Luo on May 29, 2017 at 12:49am —
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… Continue
Added by Zhongmin Luo on May 23, 2017 at 1:30am —
There are many great tutorials on neural networks that one can find online nowadays. Simply searching for the words “Neural Network” will produce numerous results on GithubGist. Even tough there are many examples floating around on the web, I decided to have my own Introduction to Neural Networks!
In my tutorial, I specifically tried to illustrate the use of Python classes to define layers in the network as objects. Each layer object has forward and backward propagation methods which… Continue
Added by Burak Himmetoglu on February 7, 2017 at 2:30pm —
This video session features the keynote speaker Professor Geoff Hinton FRS, “Deep Learning”. This lecture was filmed on May 22, 2015.
Watch full video at … Continue
Added by Diego Marinho de Oliveira on April 4, 2016 at 5:32am —