In this tutorial, you will learn how to build an R-CNN object detector using Keras, TensorFlow, and Deep Learning. Today's tutorial is the final part in our 4-part series on deep learning and object detection: What if we wanted to train an object detection network on our own custom datasets? How can we train that network using Selective Search search? And how will using Selective Search change our object detection inference script? In fact, these are the same questions that Girshick et al. had to consider in their seminal deep learning object detection paper Rich feature hierarchies for accurate object detection and semantic segmentation. Each of these questions will be answered in today's tutorial -- and by the time you're done reading it, you'll have a fully functioning R-CNN, similar (yet simplified) to the one Girshick et al. implemented! To learn how to build an R-CNN object detector using Keras and TensorFlow, just keep reading.

DeepMind recently introduced a new meta-learning approach that generates a reinforcement learning algorithm known as Learned Policy Gradient (LPG). According to the researchers, automating the discovery of update rules from data could lead to more efficient algorithms that could also be better adapted to specific environments. That one technique of machine learning which can be compared with the psychological behaviour of animals is reinforcement learning. The objective of reinforcement learning is to maximise the expected cumulative rewards or average rewards. This algorithm has gained much traction by researchers and developers over the past few years.

We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.

Sap flows in the crop are an important indicator of plant development. Students at Inholland University of Applied Sciences worked in the Ideal Research Greenhouse Lab to measure and visualize the sap flows in a cherry tomato crop, whereby Machine Deep Learning has been used to predict the growth of the head thickness of the plants based on these data, they wrote in a research report. Algorithms Within the project, which was monitored using heat-balance sensor technology, algorithms were developed that monitor the moisture balance of the plant. The sap flows (Xylem) and their balance are in practice among the most difficult to measure and to control parameters, the researchers said in the report. "Plants can be grown optimally based on proactive response to evaporation of moisture from the plant." A model has been developed to control, automate, regulate and optimize the development process of the plant while wastage of raw materials such as water, nutrients, etc. is minimized or even prevented.

After several years in leadership roles at Epic Systems and Microsoft, Raghava returned from the US in 2011 and started operations in Bangalore. Raghava is passionate about reading and playing the guitar.

This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012. The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorised Internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters – 10 times more than previous competitors – is surprisingly powerful. The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential.

Tech firms around the world are in an overdrive to help battle the coronavirus pandemic. Research showed that 6.5% of COVID-19 patients can suddenly progress to serious illness, and among them the mortality rate can be as high as 49%. Therefore one of the key tasks for health authorities is to identify and treat patients who may develop severe or fatal syndromes early on. That's what has been underway at a research team jointly established by Tencent AI Lab and a group of Chinese public health scientists headed by Zhong Nanshan, the country's senior medical advisor on COVID-19, since its inception in February. This week, the team unveiled a deep learning-based model that can predict the risk of coronavirus patients developing critical illness.

The tech industry pays programmers handsomely to tap the right keys in the right order, but earlier this month entrepreneur Sharif Shameem tested an alternative way to write code. First he wrote a short description of a simple app to add items to a to-do list and check them off once completed. Then he submitted it to an artificial intelligence system called GPT-3 that has digested large swaths of the web, including coding tutorials. "I got chills down my spine," says Shameem. "I was like, 'Woah something is different.'" GPT-3, created by research lab OpenAI, is provoking chills across Silicon Valley.

This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attacker's capability and affected stage of the machine learning pipeline, the attack surfaces are recognized to be wide and then formalized into six categorizations: code poisoning, outsourcing, pretrained, data collection, collaborative learning and post-deployment. Accordingly, attacks under each categorization are combed. The countermeasures are categorized into four general classes: blind backdoor removal, offline backdoor inspection, online backdoor inspection, and post backdoor removal. Accordingly, we review countermeasures, and compare and analyze their advantages and disadvantages.

An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this post, we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi-Layer Perceptron. ANNs are at the core of Deep Learning. They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., Google Images), powering speech recognition services (e.g., Apple's Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., Youtube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind's AlphaGo).