Creating & Visualizing Neural Network in R

Neural network is an information-processing machine and can be viewed as analogous to human nervous system. Just like human nervous system, which is made up of interconnected neurons, a neural network is made up of interconnected information processing units. The information processing units do not work in a linear manner. In fact, neural network draws its strength from parallel processing of information, which allows it to deal with non-linearity. Neural network becomes handy to infer meaning and detect patterns from complex data sets. Neural network is considered as one of the most useful technique in the world of data analytics. However, it is complex and is often regarded as a black box, i.e. users view the input and output of a neural network but remain clueless about the knowledge generating process. We hope that the article will help readers learn about the internal mechanism of a neural network and get hands-on experience to implement it in R.

Building Machine Learning Model is fun using Orange

In the growing market of Data Science, there are quite some details that people miss out on. These are tools or techniques that can make you a better performer in the field and also ease your efforts and help you focus on the analytics rather than the trivialities. Here, I will introduce you to another GUI based tool – Orange. This tool is great for beginners who wish to visualize patterns and understand their data without really knowing how to code. In my previous article, I presented you another GUI based tool KNIME, follow this link to learn about it further. https://…/knime-machine-learning By the end of this tutorial, you’ll be able to predict which person out of a certain set of people is eligible for a loan with Orange!

How to Train a Final Machine Learning Model

The machine learning model that we use to make predictions on new data is called the final model.
There can be confusion in applied machine learning about how to train a final model.
This error is seen with beginners to the field who ask questions such as:
• How do I predict with cross validation?
• Which model do I choose from cross-validation?
• Do I use the model after preparing it on the training dataset?
This post will clear up the confusion.
In this post, you will discover how to finalize your machine learning model in order to make predictions on new data.

Text Message Classification

Classification is a supervised machine learning technique in which the dataset which we are analyzing has some inputs X i Xi and a response variable Y Y which is a discrete valued variable.Discrete valued means the variable has a finite set of values.In more specific terms in classification the response variable has some categorical values.In R we call such values as factor variables. For example-Y Y (Male,Female) or (0,1) or (High,low,medium) etc are the best examples of the response variable Y Y in a classification problem.

RStudio Connect v1.5.6 – Now Supporting Kerberos!

We’re pleased to announce support for Kerberos in RStudio Connect: version 1.5.6. Organizations that use Kerberos can now run Shiny applications and Shiny R Markdown documents in tailored processes that have access only to the appropriate resources inside the organization.

Time Series Prediction Using Recurrent Neural Networks (LSTMs)

The Statsbot team has already published the article about using time series analysis for anomaly detection. Today, we’d like to discuss time series prediction with a long short-term memory model (LSTMs). We asked a data scientist, Neelabh Pant, to tell you about his experience of forecasting exchange rates using recurrent neural networks.

Pseudo-labeling a simple semi-supervised learning method

The foundation of every machine learning project is data – the one thing you cannot do without. In this post, I will show how a simple semi-supervised learning method called pseudo-labeling that can increase the performance of your favorite machine learning models by utilizing unlabeled data.

The Total Newbie’s Guide to Cassandra

Cassandra is a distributed NoSQL data storage system from Apache that is highly scalable and designed to manage very large amounts of structured data. It provides high availability with no single point of failure.

Bloom embedding layers

Large embedding layers are a performance problem for fitting models: even though the gradients are sparse (only a handful of user and item vectors need parameter updates in every minibatch), PyTorch updates the entire embedding layer at every backward pass. Computation time is then wasted on applying zero gradient steps to whole embedding matrix. To alleviate this problem, we can use a smaller underlying embedding layer, and probabilistically hash users and items into that smaller space. With good hash functions, collisions should be rare, and we should observe fitting speedups without a decrease in accuracy.

German Tech Company DeepL Launches DeepL Translator

Despite the money and resources of major internet companies, it is DeepL that can boast the world’s most accurate and natural-sounding machine translation tool. When users enter a text, DeepL is able to capture even the slightest nuances and reproduce them in translation unlike any other service. From today, it is available to everyone free of charge.

Julia: A High-Level Language for Supercomputing and Big Data

The key software stack used for high-performance computing (HPC) workloads has typically been a statically compiled language such as C/C++ or Fortran, in conjunction with OpenMP/MPI. This environment has stood the test of time and is almost ubiquitous in the HPC world. The key reason for this is its efficiency in terms of the ability to use all available compute resources while limiting memory usage. This level of efficiency has always been beyond the scope of more “high-level” scripting languages such as Python, Matlab/Octave, or R. This is primarily because such languages are designed to be high-productivity environments that facilitate rapid prototyping?and are not designed to run efficiently on large compute clusters. However, there is no technical reason why this should be the case, and this represents a tooling problem for scientific computing.

Overfitting in Machine Learning: What It Is and How to Prevent It

Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit?

I built a chatbot in 2 hours and this is what I learned

We spend about 5 hours on our smartphones every day as per this study from Flurry. Not only is this statistic surprising in its own right, about 65% of this time is spent on communication related activities like social media, texting, emailing and phone calls. That’s 3 hours and 15 minutes. Every. Single. Day. What it means is that the tables have turned at an angle acute. The mobile app that you were building for your kickass startup idea? It’s going to compete with millions of other apps for just 35% of the user’s daily attention. And not to forget the discovery costs associated with it.

Decoding AI

Everywhere you look people seem to be talking about one thing. Well, I mean apart from Donald Trump and his disturbingly entertaining antics. And that’s AI. There are those that can’t contain their excitement (‘Yo, AI is going to change the world!’). There are those that are uncertain (‘We really don’t yet fully understand the impact AI would have on everything’). And finally, those that have declared apocalypse is upon us (‘AI will eat up humanity. Prepare to perish!’). It has in fact become a much abused term, being used out of context and often erroneously for a lot of things.

Free Energies and Variational Inference

My graduate advisor used to say: “If you can’t invent something new, invent your own notation” Varitional Inference is foundational to Unsupervised and Semi-Supervised Deep Learning. In particular, Variational Auto Encoders (VAEs). There are many, many tutorials and implementations on Variational Inference, which I collect on my YouTube channel and below in the references. In particular, I look at modern ideas coming out of Google Deep Mind.

The Ultimate Guide To Partitioning Clustering

Data Science for Fraud Detection

Fraud can be defined as “the crime of getting money by deceiving people” (Cambridge Dictionary); it is as old as humanity: whenever two parties exchange goods or conduct business, there is the potential for one party scamming the other. With an ever-increasing use of the internet for shopping, banking, filing insurance claims etc., these businesses have become targets of fraud in a whole new dimension. Fraud has become a major problem in e-commerce and a lot of resources are being invested to recognize and prevent it.