In this Guided Project, you will: … Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. In this 1-hour long project-based course, you will be able to: – Understand the theory and intuition behind Convolutional Neural Networks (CNNs). Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.2

Deep learning continues to be one of the hottest fields in computing, and while Google's TensorFlow remains the most popular framework in absolute numbers, Facebook's PyTorch has quickly earned a reputation for being easier to grasp and use. PyTorch has taken the world of deep learning research by storm, outstripping TensorFlow as the implementation framework of choice in submitted papers for AI conferences in the past two years. With recent improvements for producing optimized models and deploying them to production, PyTorch is definitely a framework ready for use in industry as well as R&D labs. But how to get started? You'll find plenty of books and paid resources available for learning PyTorch, of course.

The practice to include Artificial Intelligence in industry application is skyrocketing for a decade now. It is evident since, AI and its constituent applications Machine Learning, computer vision, facial analysis, autonomous vehicles, deep learning form the pillars of modern digital empowerment. The ability to learn the data it is trained up to understand the binary, quantum computation of the world, and make decisions derived from its insights makes AI unique than earlier technologies. Leaders believe that possessing AI-based technologies equate to future industry successes. From healthcare, research, finance, logistics to military, law enforcement department AI holds the key to massive competitive edge and up-gradation with monetary benefits too.

We know that Artificial Intelligence (AI) is the main force moving the society into the future described in the movies in the past couple of decades. There are new heights achieved every day in different fields using Artificial Intelligence methods. Artificial Intelligence is a huge field and contains a lot of subfields, that contain a lot of subfields themselves. There are huge amounts of sources that claim that can help you learn Artificial Intelligence. These sources come in different forms and types, from books, blogs, projects, videos, etc. Today we are going to talk about the video sources, more precisely YouTube videos.

Ancient Greek philosophers had spent much of their time pondering about what truly makes one intelligent. But this concept was embraced in science and research only about half a century ago. Ever since its inception, neuroscience has strived to understand how the brain processes information, makes decisions, and interacts with the environment. But in the mid – 20th century, arose a new school of thought – how can we emulate intelligence in an artificial system? This does sound daunting and can definitely leave the few odd eccentric minds wondering about its dystopian implications.

Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object. These boxes are then filtered using non-maximum suppression (NMS) in order to select exactly one bounding box per object of interest. This greedy scheme is simple and provides sufficient accuracy for isolated objects but often fails in crowded environments, since one needs to both preserve boxes for different objects and suppress duplicate detections. In this work we develop an alternative iterative scheme, where a new subset of objects is detected at each iteration. Detected boxes from the previous iterations are passed to the network at the following iterations to ensure that the same object would not be detected twice.

"Whenever you compete, you have to accept simple rules – someone wins, someone loses, and usually the winner takes it all." For this week's ML practitioner's series, Analytics India Magazine got in touch with Oleg Yaroshevskiy from Ukraine. In this interview, he shares his experiences from his journey to the top 20 in one of the toughest data science competitions in the world. Oleg majored in maths and statistics from Cybernetics Faculty of Taras Shevchenko National University of Kyiv, which was co-founded by Victor Glushkov, one of the cybernetics pioneers who played a key role in the advancement of theoretical computer science, including artificial intelligence. Oleg had a formal introduction to machine learning (ML) during his graduation days where he had studied neural networks along with the popular Andrew NG's course on Coursera back in 2013.

Often, the layers in deep convolution networks have an increasing number of filters from the first layer to the last. Also, the kernel size goes down or stays the same in some models. In the early convolution networks like the AlexNet, the authors used huge filter sizes (11 11 kernels in the first layer of AlexNet). And they gradually reduced the kernel size for the layers deep in the network. This is due to the increased receptive field of the later layers on the input image. While having more filters and a huge number of layers helps the model capture more abstract features, it increases the model's size among other problems.

You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels. As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available.

While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to deep graph similarity learning. Recent work has considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich cross-level interactions (e.g., between nodes and a whole graph). In this paper, we propose a Hierarchical Graph Matching Network (HGMN) for computing the graph similarity between any pair of graph-structured objects. Our model jointly learns graph representations and a graph matching metric function for computing graph similarities in an end-to-end fashion. The proposed HGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs. Our comprehensive experiments demonstrate that HGMN consistently outperforms state-of-the-art graph matching network baselines for both classification and regression tasks.