Join our upcoming webinar'Getting Started With AI-Optimised Architecture' on Thursday 6th June at 11 am. In this webinar, we will be joined by Chris Parsons, AI Technical Lead at IBM to explain why it's important to optimise your business infrastructure and why the hardware matters for successful AI projects. All attendees will qualify for CSI's Identify & Imagine AI Workshop – the ideal next step in defining your goals and starting your AI strategy. Register now to find out how CSI can help you get a competitive edge with cognitive solutions. Please fill in the fields below to register for this event.

A new report highlights an artificial intelligence (AI) knowledge gap in public relations. The CIPR is helping its members plug the gap. It's too early to identify best practice for AI communication. Practitioners need to step up and take personal responsibility for learning and development. This includes exploring the impact of AI on society and the communication workflow.

In this tutorial, you will learn how to perform transfer learning with Keras, Deep Learning, and Python on your own custom datasets. You're just hired by Yelp to work in their computer vision department. Yelp has just launched a new feature on its website that allows reviewers to take photos of their food/dishes and then associate them with particular items on a restaurant's menu. Certain nefarious users aren't taking photos of their dishes…instead, they are taking photos of… (well, you can probably guess). Figure out how to create an automated computer vision application that can distinguish between "food" and "not food", thereby allowing Yelp to continue with their new feature launch and provide value to their users. So, how are you going to build such an application? The answer lies in transfer learning via deep learning. Today marks the start of a brand new set of tutorials on transfer learning using Keras.

In this tutorial, you will learn how to perform transfer learning with Keras, Deep Learning, and Python on your own custom datasets. You're just hired by Yelp to work in their computer vision department. Yelp has just launched a new feature on its website that allows reviewers to take photos of their food/dishes and then associate them with particular items on a restaurant's menu. Certain nefarious users aren't taking photos of their dishes…instead, they are taking photos of… (well, you can probably guess). Figure out how to create an automated computer vision application that can distinguish between "food" and "not food", thereby allowing Yelp to continue with their new feature launch and provide value to their users. So, how are you going to build such an application? The answer lies in transfer learning via deep learning. Today marks the start of a brand new set of tutorials on transfer learning using Keras.

In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estimator (SURE). We define overfitting, underfitting, and generalization using the obtained true and generalization errors. We introduce cross validation and two well-known examples which are $K$-fold and leave-one-out cross validations. We briefly introduce generalized cross validation and then move on to regularization where we use the SURE again. We work on both $\ell_2$ and $\ell_1$ norm regularizations. Then, we show that bootstrap aggregating (bagging) reduces the variance of estimation. Boosting, specifically AdaBoost, is introduced and it is explained as both an additive model and a maximum margin model, i.e., Support Vector Machine (SVM). The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting. As examples of regularization, the theory of ridge and lasso regressions, weight decay, noise injection to input/weights, and early stopping are explained. Random forest, dropout, histogram of oriented gradients, and single shot multi-box detector are explained as examples of bagging in machine learning and computer vision. Finally, boosting tree and SVM models are mentioned as examples of boosting.

I know You have knowledge of building a machine learning model. It requires many steps like data cleaning, data reduction, model creation, and other steps. Each time you define a problem on it, you repeat all the steps to make a better model. But wait do you know you can automate these steps?. If yes then you can read our other posts.

Bots (short for software robots) are software applications that perform often repetitive or simple tasks. In particular, social and chat bots interacting with humans are a recent research topic. Similarly, bots can be used to automate many tasks that are performed by software practitioners and teams in their day-to-day work. Recent work argue that bots can save developers' time and significantly increase productivity. Therefore, the goal of this one-day workshop is to bring together software engineering researchers and practitioners to discuss the opportunities and challenges of bots in software engineering.

Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. In recent years, deep learning techniques are achieving state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Notable is the "You Only Look Once," or YOLO, family of Convolutional Neural Networks that achieve near state-of-the-art results with a single end-to-end model that can perform object detection in real-time. In this tutorial, you will discover how to develop a YOLOv3 model for object detection on new photographs. How to Perform Object Detection With YOLOv3 in Keras Photo by David Berkowitz, some rights reserved.

For the purpose of entrusting all sentient beings with powerful AI tools to learn, deploy and scale AI in order to enhance their prosperity, to settle planetary-scale problems and to inspire those who, with AI, will shape the 21st Century, Montréal.AI introduces the "VIP AI 101 CheatSheet for All". Encompassing all facets of AI, the General Secretariat of MONTREAL.AI presents, with authority and from insider knowledge: "Artificial Intelligence 101: The First World-Class Overview of AI for the General Public". You are qualified for a career in machine learning! Language: Course given in English. "In life, you need forcing functions. You never know what you're capable of until you have no choice but go and do it. Excessive comfort leads to unrealized potential."

In this step-by-step Keras tutorial, you'll learn how to build a convolutional neural network in Python! In fact, we'll be training a classifier for handwritten digits that boasts over 99% accuracy on the famous MNIST dataset. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. Our goal is to introduce you to one of the most popular and powerful libraries for building neural networks in Python. That means we'll brush over much of the theory and math, but we'll also point you to great resources for learning those.