In practice, by simply training a generator and a discriminator together consisting of multi-layer neural networks with non-linear activation functions, using local search algorithms such as stochastic gradient descent ascent (SGDA), the generator network can be trained efficiently to generate samples from highly-complicated distributions (such as the distribution of images). Despite the great empirical success of GAN, it remains to be one of the least understood models on the theory side of deep learning. Most of existing theories focus on the statistical properties of GANs at the global-optimum [15, 16, 20, 87]. However, on the training side, gradient descent ascent only enjoys efficient convergence to a global optimum when the loss function is convex-concave, or efficient convergence to a critical point in general settings [37, 38, 48, 53, 71, 73, 75, 77, 78]. Due to the extreme non-linearity of the networks in both the generator and the discriminator, it is highly unlikely that the training objective of GANs can be convex-concave. In particular, even if the generator and the discriminator are linear functions over prescribed feature mappings-- such as the neural tangent kernel (NTK) feature mappings [3, 8, 9, 17, 18, 32, 35, 40, 41, 47, 51, 54, 65, 69, 92, 97] -- the training objective can still be non-convex-concave.

Artificial Intelligence has revolutionized many industries in the past decade, and healthcare is no exception. In fact, the amount of data in healthcare has grown 20x in the past 7 years, causing an expected surge in the Healthcare AI market from $2.1 to $36.1 billion by 2025 at an annual growth rate of 50.4%. AI in Healthcare is transforming the way patient care is delivered, and is impacting all aspects of the medical industry, including early detection, more accurate diagnosis, advanced treatment, health monitoring, robotics, training, research and much more. In light of the worldwide COVID-19 pandemic, there has never been a better time to understand the possibilities of artificial intelligence within the healthcare industry and learn how you can make an impact to better the world's healthcare infrastructure. Artificial Intelligence has revolutionized many industries in the past decade, and healthcare is no exception.

What you can't find in someone's voice, you might find in someone's writing. I was always more inclined to following and referring video tutorials/lectures whenever it comes down to studying something on my own from the web. I found it easier ( just like some of you) to understand and not go through the pain of reading available books. Most of the time, I felt the same unless recently I discovered those writers or publishers who eliminated the element of'bore' from subject books and made them so… much interesting. This started when one of my really smart friends told me to start reading books because they contain more content and adds to a really important skill for any person, that is reading and understanding.

It's used for speech recognition, machine translation, computer vision and natural language processing. Deep Learning has applications in medical diagnosis, server optimisation, data centre security, autonomous driving and more. Below, we have listed down seven resources to learn Deep Learning. The Association of Data Scientists offers online courses to provide in-depth knowledge of various areas within machine learning and data science. Most of these courses are available as videos for self-paced learning along with relevant Colab notebooks.

Welcome to the course where we will learn about Artificial Neural Network (ANN) From Scratch! If you're looking for a complete Course on Deep Learning using ANN that teaches you everything you need to create a Neural Network model in Python? You've found the right Neural Network course! This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy.

In the last decade, the demand for artificial intelligence programmers has increased exponentially, both in Mexico and throughout the world. According to Gartner, sectors such as energy, retail, financial services, telecommunications and manufacturing, are the most predisposed to take advantage of artificial intelligence in Mexico. Precisely Donald Feinberg, research director at Gartner, specializing in the area of artificial intelligence (AI), assures that in the country this field of information technology is reaching a very important role, as important as the one it already has in the United States. However, according to the National Institute of Statistics and Geography (INEGI), in the country there are 976 thousand people trained in computing or information and communication technologies, of which 241 thousand, at least, do not have a related job to the race. For this reason, it is becoming increasingly necessary to carry out training, through which the knowledge and skills required by emerging technologies, such as artificial intelligence, are obtained, and thus be able to aspire to the jobs offered by different companies.

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning.

This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 20 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits! What do you get with Early Bird Access? You will get exclusive access to weekly live video streams where we will go through interactive machine learning projects! You'll be able to directly ask questions during the streams that will coincide with section launches corresponding to new machine learning algorithms added to the course content! These weekly streams will also include live Q&A with the instructor of the course, Jose Portilla.

In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is twofold: first, to provide a broad range of capabilities to streamline as well as foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle; second, to encourage further exploration of UQ's connections to other pillars of trustworthy AI such as fairness and transparency through the dissemination of latest research and education materials. Beyond the Python package (\url{https://github.com/IBM/UQ360}), we have developed an interactive experience (\url{http://uq360.mybluemix.net}) and guidance materials as educational tools to aid researchers and developers in producing and communicating high-quality uncertainties in an effective manner.

Recent works leveraging Graph Neural Networks to approach graph matching tasks have shown promising results. Recent progress in learning discrete distributions poses new opportunities for learning graph matching models. In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem. Our model defines a distribution of matchings for a graph pair so the model can explore a wide range of possible matchings. We further introduce a novel multi-step matching procedure, which learns how to refine a graph pair's matching results incrementally. The model also includes dummy nodes so that the model does not have to find matchings for nodes without correspondence. We fit this model to data via scalable stochastic optimization. We conduct extensive experiments across synthetic graph datasets as well as biochemistry and computer vision applications. Across all tasks, our results show that SIGMA can produce significantly improved graph matching results compared to state-of-the-art models. Ablation studies verify that each of our components (stochastic training, iterative matching, and dummy nodes) offers noticeable improvement.