The Indian Institute of Science (IISc), Bangalore is organizing an Online Workshop on AI & IoT for Flow Modeling on November 20, 2020, from 11 am to 2 pm. The Indian Institute of Science (IISc) is a public, deemed, research university for higher education and research in science, engineering, design, and management. It is located in Bangalore (Bengaluru), in the Indian state of Karnataka. The workshop is organized as part of the Indo-Dutch project, "Digital Twins for pipeline transport networks". The aim of the project is to develop a digital twin that connects sensor data and advanced fluid solvers in order to detect possible leakage of fluid from the pipeline in real-time.

Free Coupon Discount Preview this course Udemy -  Artificial Intelligence A-Z™: Learn How To Build An AI, Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, SuperDataScience Support

Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects throughout the course. This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, excited, and robust projects to better understand each concept under each topic.

Preview this course - GET COUPON CODE Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot more I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Here is the details about the project. Here we will star from colab understating because that will help to use free GPU provided by google to train up our model. We're going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception.

Researchers from Imperial College London and the Swiss Federal Laboratories for Materials Science ... [ ] and Technology have proposed the combined discipline of "Physical AI" as a means for developing lifelike autonomous robots. Researchers at Imperial College London have proposed "physical artificial intelligence" as a new multidisciplinary area of research that could be vital to producing lifelike intelligent robots in the future. Writing in the Nature Machine Intelligence journal, the team argue that teaching materials science, mechanical engineering, computer science, biology and chemistry as a combined discipline would help students and researchers develop lifelike artificially intelligent robots. This combined discipline of "physical AI" could effectively be the missing link in the attempt to create artificially intelligent robots that look and behave like humans, the Imperial College London team suggests. They argue that research into how to build lifelike robot bodies has failed to keep up with advances in computational artificial intelligence, and that the study and practice of physical artificial intelligence could rectify this imbalance.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc.This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.

Preview this course - GET COUPON CODE Learn how to build Machine Learning projects in this TensorFlow Course created by The Click Reader. In this course, you will be learning about Scalar as well as Tensors and how to create them using TensorFlow. You will also be learning how to perform various kinds of Tensor operations for manipulating and changing tensor values. You will be performing a total of three Machine Learning projects while learning through this TensorFlow full course: 1. Linear Regression from Scratch You will be learning how to create a Linear Regression model from scratch using TensorFlow. You will be preparing the data, building the model architecture as well as training the model using a custom-made loss function as well as an optimizer.

Created by Lazy Programmer Inc. English [Auto-generated], Indonesian [Auto-generated], 5 more Created by Lazy Programmer Inc. Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit.

In this tutorial you will learn how to perform super resolution in images and real-time video streams using OpenCV and Deep Learning. Today's blog post is inspired by an email I received from PyImageSearch reader, Hisham: "Hi Adrian, I read your Deep Learning for Computer Vision with Python book and went through your super resolution implementation with Keras and TensorFlow. It was super helpful, thank you. Are there any pre-trained super resolution models compatible with OpenCV's dnn module? Can they work in real-time? If you have any suggestions, that would be a big help."