Announcing new Deep Learning courses on Coursera

I have been working on three new AI projects, and am thrilled to announce the first one:, a project dedicated to disseminating AI knowledge, is launching a new sequence of Deep Learning courses on Coursera. These courses will help you master Deep Learning, apply it effectively, and build a career in AI.

10 Advanced Deep Learning Architectures Data Scientists Should Know!

It is becoming very hard to stay up to date with recent advancements happening in deep learning. Hardly a day goes by without a new innovation or a new application of deep learning coming by. However, most of these advancements are hidden inside the large amount of research papers that are published on mediums like ArXiv / Springer To keep ourselves updated, we have created a small reading group to share our learnings internally at Analytics Vidhya. One such learning I would like to share with the community is a a survey of advanced architectures which have been developed by the research community.

27 Great Resources About Decision Trees

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more.

Understanding Neural Network: A beginner’s guide

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.

Data Science Simplified Part 5: Multivariate Regression Models

In the last article of this series, we discussed the story of Fernando. A data scientist who wants to buy a car. He uses Simple Linear Regression model to estimate the price of the car.

Data Science Simplified Part 4: Simple Linear Regression Models

In the previous posts of this series, we discussed the concepts of statistical learning and hypothesis testing. In this article, we dive into linear regression models. Before we dive in, let us recall some important aspects of statistical learning.

Apache Spark Streaming

A data stream is an unbounded sequence of data arriving continuously. Streaming divides continuously flowing input data into discrete units for further processing. Stream processing is low latency processing and analyzing of streaming data. Spark Streaming was added to Apache spark in 2013, an extension of the core Spark API that provides scalable, high-throughput and fault-tolerant stream processing of live data streams. Data ingestion can be done from many sources like Kafka, Apache Flume, Amazon Kinesis or TCP sockets and processing can be done using complex algorithms that are expressed with high-level functions like map, reduce, join and window. Finally, processed data can be pushed out to filesystems, databases and live dashboards. Its internal working is as follows. Live input data streams is received and divided into batches by Spark streaming, these batches are then processed by the Spark engine to generate the final stream of results in batches. Its key abstraction is Apache Spark Discretized Stream or, in short, a Spark DStream, which represents a stream of data divided into small batches. DStreams are built on Spark RDDs, Spark’s core data abstraction. This allows Streaming in Spark to seamlessly integrate with any other Apache Spark components like Spark MLlib and Spark SQL.

Tutorial: Publish an R function as a SQL Server stored procedure with the sqlrutils package

In SQL Server 2016 and later, you can publish an R function to the database as a stored procedure. This makes it possible to run your R function on the SQL Server itself, which makes the power of that server available for R computations, and also eliminates the time required to move data to and from the server. It also makes your R function available as a resource to DBAs for use in SQL queries, even if they don’t know the R language.

Seeking guidance in choosing and evaluating R packages

At useR!2017 in Brussels last month, I contributed to an organized session focused on navigating the 11,000+ packages on CRAN. My collaborators on this session and I recently put together an overall summary of the session and our goals, and now I’d like to talk more about the specific issue of learning about R packages and deciding which ones to use. John and Spencer will write more soon about the two other issues of our focus:
• meta-packages that can unify multiple packages within domains and
• searching for packages.
In preparation for this session, I ran a brief online survey in the spring of 2017 to ask R users how they currently discover and learn about R packages. The results of this survey are available in an R package (SO META) on GitHub.

Integrating data with AI

As companies have embraced the idea of data-driven management, many have discovered that their hard-won stores of valuable data are badly siloed: separate troves of data live in different parts of the company on separate systems. These data sets may relate to each other in essence, but they’re often difficult to integrate because they differ slightly in schemas and data definitions. In this podcast episode, I speak with Eliot Knudsen, data science lead at Tamr, a company that uses AI to integrate data across silos. Data integration is often a painstaking, highly manual process of matching fields and resolving entities, but new tools can work alongside human experts to discover patterns in data and make recommendations for automatically merging it.

Deep learning revolutionizes conversational AI

The dream of speech recognition is a system that truly understands humans speaking—in different environments, with a variety of accents and languages. For decades, people tackled this problem with no success. Pinpointing effective strategies for creating such a system seemed impossible. In the past years, however, breakthroughs in AI and deep learning have changed everything in the quest for speech recognition. Applying deep learning techniques enabled remarkable results. Today, we see the leap forward in development manifesting in a wide range of products, such as Amazon Echo, Apple Siri, and many more. In this post, I’ll review recent advances in speech recognition, examine the elements that have contributed to the rapid progress, and discuss the futureand how far we may be from solving the problem completely.

Overview of Natural Language Generation (NLG)

NLG (Natural Language Generation), a subfield of Artificial Intelligence, is a hot topic in the technology news today. We hear a lot about AI that can soon replace writers and journalists beginning the era of machine creativity. But, what’s all this fuss about? In this article, we unveil what NLG really is and show that it can bring a lot of benefits to businesses and consumers.

Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings

How can artificial neural nets help in understanding our brain’s neural net? On the weekend of March 24-26, YCombinator-backed startup DeepGram hosted a deep learning hackathon. The weekend-long event included speakers and judges from Google Brain, NVIDIA, and Baidu. My colleague, Dr. Matt Rubashkin, also participated and you can read about his project here. I chose to work on one of the datasets suggested by DeepGram: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis. Winning Kaggle competition teams have successfully applied artificial neural networks on EEG data (see first place winner of the grasp-and-lift challenge and third place winner of seizure prediction competition). Could a neural net model do better on this Stanford data set?