In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.

NEW DELHI: Bennett University launched an artificial intelligence (AI) initiative in association with 50 zonal partners to set up research groups across the country. The project was inaugurated by All India Council for Technical Education (AICTE) chairman Anil Sahsrabudhe at the Bennett University campus on Tuesday. The zonal partners will be doubled to 100 with AICTE support. Each zonal partner will work in the area of deep learning to create useful products and innovations. Each will act as a hub for 10 more institutions, thus adding up to a total of 1,000.

This post explains how to build your first Convolutional Neural Network (CNN) to detect between two image types: for example, a bunny or a puppy. Thanks to Google's new web tool, getting started building and prototyping your own neural network can be quite easy. Here is a link to the web-based application. It shows you the code and lets you run "paragraph by paragraph" (shift enter) jupyter notebook code to let you train a model and then test it. Find the Github public repo here.

Artificial Intelligence (AI) has become very popular in recent years. Earlier AI used to be trending for expertise but now the scenario is changed it is also prevailing in IT sector. And many people are attracted by the growth found in AI field. TensorFlow is an open source software developed by Google Brain team./v. It is used to carry out numerical computations and is also useful in machine learning. This framework can compute on any CPUs, GPUs, TPUs or any desktop or edge devices.

Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch is one such library. In the last few weeks, I have been dabbling a bit in PyTorch. I have been blown away by how easy it is to grasp. Among the various deep learning libraries I have used till date – PyTorch has been the most flexible and effortless of them all.

The following is a list of free books on Machine Learning. A Brief Introduction To Neural Networks provides a comprehensive overview of the subject of neural networks and is divided into 4 parts –Part I: From Biology to Formalization -- Motivation, Philosophy, History and Realization of Neural Models,Part II: Supervised learning Network Paradigms, Part III: Unsupervised learning Network Paradigms and Part IV: Excursi, Appendices and Registers. A Course In Machine Learning is designed to provide a gentle and pedagogically organized introduction to the field and provide a view of machine learning that focuses on ideas and models, not on math. The audience of this book is anyone who knows differential calculus and discrete math, and can program reasonably well. An undergraduate in their fourth or fifth semester should be fully capable of understanding this material. However, it should also be suitable for first year graduate students, perhaps at a slightly faster pace.

The Adversarial Robustness Toolbox (ART), an open source software library, supports both researchers and developers in defending deep neural networks against adversarial attacks, making AI systems more secure. Its purpose is to allow rapid crafting and analysis of attack and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. It is designed to support researchers and AI developers in creating novel defense techniques and in deploying practical defenses of real-world AI systems. For AI developers, the library provides interfaces that support the composition of comprehensive defense systems using individual methods as building blocks.

Is Data-Driven AI Brainwashing us all, or is it Just the Same as Good ol' Marketing? April 15, 2018 The many claims made as part of the recent Cambridge Analytica & Facebook scandal, reviewed Can a "Google AI" Build Your Genome Sequence? March 31, 2018 A new AI-powered tool from Google promises more-accurate genome sequences, but its impact on genomics research remains to be seen Deepfakes - Is Seeing Still Believing? March 29, 2018 Has widespread misuse of AI arrived? OpenAI's Not So Open DotA AI February 10, 2018 An impressive demo by OpenAI leaves many questions unanswered The Crazy Coverage of Facebook's Unremarkable'AI Invented Language' August 12, 2017 Sometimes the narratives media conjures up just serve to make real life seem boring The Curious Case of OpenAI's Unsupervised Sentiment Neuron April 18, 2017 A nifty thing happened unintentionally, and some people overreacted AlphaGo - so is Human Intelligence Obsolete?

These days, you hear a lot about machine learning (or ML) and artificial intelligence (or AI) – both good or bad depending on your source. Many of us immediately conjure up images of HAL from 2001: A Space Odyssey, the Terminator cyborgs, C-3PO, or Samantha from Her when the subject turns to AI. And many may not even be familiar with machine learning as a separate subject. The phrases are often tossed around interchangeably, but they're not exactly the same thing. In the most general sense, machine learning has evolved from AI. In the Google Trends graph above, you can see that AI was the more popular search term until machine learning passed it for good around September 2015.

About the conference The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that exemplify a balance of high-level rigor and business know-how, as well as elevating the role of analytics in an organization's strategic decision-making. WCAI will host not only the full day event on May 10th, but also technical workshops the day before, on May 9th. This year, there are two workshops from 2:00 p.m. – 5;00 p.m. for attendees: Workshop Overview: Deep learning plays a significant role in sentiment analysis, where algorithms can be trained to quickly learn and detect patterns in large volumes of data. In this workshop, we will start by providing an overview on deep learning and on the Apache MXNet deep learning framework. We will next discuss how to address sentiment analysis use cases with deep learning.