Recently, Graph Neural Networks have gained increasing attention from the Machine Learning researchers and the community. With its strong expressiveness, they are likely to be the next game-changing Neural Networks. PyTorch Geometric is one of the fastest Graph Neural Networks frameworks in the world. This article about the basic usage of PyTorch Geometric and how to use it on real-world data.
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you'll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.
The NVIDIA Deep Learning Institute offers self-paced classes for deep learning that feature interactive lectures, hands-on exercises, and live Q&A with instructors. You'll learn everything you need to design, train, and integrate neural network-powered artificial intelligence into your applications with widely used open-source frameworks and NVIDIA software. During the hands-on exercises, you will use GPUs and deep learning software in the cloud. This is an introductory course, so previous experience with deep learning and GPU programming is not required. Please send your questions to DeepLearningInstitute@nvidia.com.
This episode, we are going to mention AutoML concept. Automated Machine Learning or shortly AutoML offers you to skip designing steps in machine learning including algorithm selection, designing the model and tuning hyperparameters. It can build transcendental machine learning models. The longer time you provide, the better it is. We will also have a hands-on experience with H2O AutoML.