Conversational AI is revolutionizing how businesses operate in every industry with applications like virtual agents, chatbots and assistants. Creating an intelligent and intuitive app involves quickly adapting new state-of-the-art research and deploying it in production. You can learn about the latest advancements in this area at the GPU Technology Conference, taking place October 5-9. At GTC, researchers and developers from leading institutions across the globe will share new techniques and innovations in speech recognition, natural language processing and text-to-speech technologies. At GTC, the NVIDIA Deep Learning Institute is offering instructor-led, hands-on training on how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents.

Summary: Since BERT NLP models were first introduced by Google in 2018 they have become the go-to choice. New evidence however shows that LSTM models may widely outperform BERT meaning you may need to evaluate both approaches for your NLP project. Over the last year or two, if you needed to bring in an NLP project quickly and with SOTA (state of the art) performance, increasingly you reached for a pretrained BERT module as the starting point. Recently however there is growing evidence that BERT may not always give the best performance. In their recently released arXiv paper, Victor Makarenkov and Lior Rokach of Ben-Gurion University share the results of their controlled experiment contrasting transfer-based BERT models with from scratch LSTM models.

"According to the filing, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks." Looks like Google won't be stopping its infamous patenting spree anytime soon. Earlier this month, Google filed a patent for capsule networks. Turing award recipient and Google researcher Geoff Hinton was named amongst the list of inventors in the filing. According to the patent filed, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks for traditional computer vision applications. Capsule networks are aimed at alleviating the extra dimensionality which surfaces with a convolutional neural network.

Natural Language Processing (NLP) is one of the most exciting fields of artificial intelligence that enables computers to understand human languages. NLP techniques are constantly evolving and promising applications are increasingly implemented by organizations to solve a wide range of problems. What exactly are companies using NLP for? What are exciting NLP techniques in a practical context and what are the challenges when applying them? We talked to thought leaders applying NLP in different industries about their favorite NLP techniques, the biggest trends, as well as opportunities and challenges of NLP in 2020.

Convolutional Neural Networks (CNNs) are considered as game-changers in the field of computer vision, particularly after AlexNet in 2012. And the good news is CNNs are not restricted to images only. They are everywhere now, ranging from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes almost inevitable in all the fields of Data Science. Even most of the Recurrent Neural Networks rely on CNNs these days.

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow.

Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person's behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the'bible' of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability.

The Fourth Industrial Revolution just took a huge step forward, thanks to a breakthrough artificial intelligence (AI) model that can learn virtually anything about the world -- and produce the content to tell us about it. The AI program is GPT-3 by OpenAI, which started out as a language model to predict the next word in a sentence and has vastly exceeded that capability. Now, drawing from voluminous data -- essentially all of Wikipedia, links from Reddit, and other Internet content -- GPT-3 has shown it can also compose text that is virtually indistinguishable from human-generated content. Asger Alstrup Palm, Area9's chief technology officer, explained that GPT-3 was tasked with testing the "scaling hypothesis" -- to see if a bigger model with ever-increasing amounts of information would lead to better performance. Although it's too early to call the scaling hypothesis proven, there are some strong indications that this is, indeed, the case. Further validating the potential of GPT-3, Microsoft recently announced it will exclusively license the model from OpenAI, with the intention of developing and delivering AI solutions for customers and creating new solutions using natural language generation.

It's way easier than you would think. Much of the content below is based on the Intro to Deep Learning with PyTorch course by Facebook AI. If you want to learn more, take the course, or just take a look here. Below is a graph that determines whether or not a student will be accepted into a university. Two pieces of data have been used: grades and tests each on a scale of 0–10.

Convolutional Neural Networks (CNNs) have shown impressive state-of-the-art performance on multiple standard datasets, and no doubt they have been instrumental in the development and research acceleration around the field of image processing. Researchers often have a problem of getting too wrapped in the closed world of theory and perfect datasets. Unfortunately, chasing extra fractions of percentage points on accuracy is actually counterproductive to the real usages of image processing: the real world. When algorithms and methods are designed with the noiseless and perfectly predictable world of a dataset in mind, they very well may perform poorly in the real world. This has certainly shown to be the case.