I can't stress this enough: If you haven't already taken a machine learning course, take this one. This is the first online course I ever took, and it's the reason that 746,000 other students and I are in data science today. The course was created by data scientist Kirill Eremenko. What's better than his soothing voice is the actual material. Kirill teaches you all the knowledge you need to have a basic understanding of some of the most powerful and useful machine learning models. This includes models in regression, classification, clustering, reinforcement learning, natural language processing, and deep learning.

AI-powered increases in safety, productivity, and efficiency are already improving our world, and the best is yet to come! As it becomes increasingly evident how impactful AI can be, demand for employees with AI skills increases--demand is in fact already skyrocketing. The AI Programming with Python Nanodegree program makes it easy to learn the in-demand skills employers are looking for. You'll learn foundational AI programming tools (Python, NumPy, PyTorch) and the essential math skills (linear algebra and calculus) that will enable you to start building your own AI applications in just three months. Whether you're seeking a full-time role in an AI-related field, want to start applying AI solutions in your current role, or simply want to start learning the defining technology of our time, this is the perfect place to get started.

Explainability techniques are currently developed and incorporated by machine learning engineers, and not surprisingly, their needs (and companies' desire to avoid legal trouble) are being prioritized.Realizing a broader set of XAI objectives will require both greater awareness of their existence and a shift in incentives for accomplishing them. XAI standards and policy guidelines should explicitly include the needs of users, stakeholders, and impacted communities to incentivize this shift. Explainability case studies are one pedagogical tool that can help practitioners and educators understand and develop more holistic explainability strategies. Diverse organizational guidance documents, recommendations, and high-level frameworks can also help guide an organizations' executives and/or developers through key questions to support explainability that is useful and relevant to different stakeholders. While there has been some work done to evaluate AI explanations, most attempts are either computationally expensive or only focus on a small subset of what constitutes a "good explanation" and fail to capture other dimensions.

The weightwatcher tool can detect the signatures of overtraining in specific layers of a pre/trained Deep Neural Networks. In the Figure above, fig (a) is well trained, whereas fig (b) may be over-trained. That orange spike on the far right is the tell-tale clue; it's what we call a Correlation Trap. Weightwatcher can detect the signatures of overtraining in specific layers of a pre/trained Deep Neural Networks. In this post, we show how to use the weightwatcher tool to do this.

A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to both academics and practitioners. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, model evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

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Neural networks are the backbone of algorithms that predict consumer demand, estimate freight arrival time, and more. At a high level, they're computing systems loosely inspired by the biological networks in the brain. Neural networks began rising to prominence in 2010, when it was shown that GPUs make backpropagation feasible for complex neural network architectures. Between 2009 and 2012, neural networks began winning prizes in contests, approaching human-level performance on various tasks, initially in pattern recognition and machine learning. Around this time, neural networks won multiple competitions in handwriting recognition without prior knowledge of the languages to be learned.

Self-driving cars are transformational technology, on the cutting-edge of robotics, machine learning, and engineering. Learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world.

This code pattern explains how to classify an American Sign Language (ASL) alphabet using PyTorch and deep learning networks. It uses a pretrained model from the PyTorch models zoo and retrains the last part of the network. The code pattern uses PyTorch to build and train a deep learning model to classify images to 29 classes (26 ASL alphabet, space, Del, and nothing), which can be used later to help hard-of-hearing people communicate with others as well as with computers. The pattern uses a pretrained mobile network, defines a classifier, and connects it to network. It then trains this classifier along with some of the last blocks of the network on the data set.

It's wonderful to be in an area like computer science because, as we expand its reach, we encounter problems for which we don't even have the appropriate abstractions to be able to think about them. When you imagine the future of the field, what areas do you think hold the most promise? AHO: That is a great question. Particularly with fields like AI, we're starting to replace people who do routine cognitive jobs with computer programs. What will the job market of the future be with this increasing capacity and power of computing?