AssemblyAI is a remote-first AI company building powerful deep learning models for developers, startups, and enterprises to transcribe and understand their audio data. Our ASR models already outperform companies like Google, AWS, and Microsoft – which is why hundreds of companies and thousands of developers are using our APIs to transcribe and understand millions of videos, podcasts, phone calls, and zoom meetings every day. Our APIs power innovative products like conversational intelligence platforms, zoom meeting summarizers, content moderation, and automatic closed captioning. AssemblyAI's Speech-to-Text APIs are already trusted by Fortune 500s, startups, and thousands of developers around the world, with well-known customers including Spotify, Algolia, Dow Jones, Happy Scribe, BBC, The Wall Street Journal, and NBCUniversal. As part of a huge and emerging market, AssemblyAI is well on its way to becoming the leader in speech recognition and NLP.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Most of the recently trending technologies such as BERT, GPT-3, Transformers, LSTM, GANs and others have deep learning at the core. These deep learning-based applications are transforming many industries such as self-driving, language translation, fraud detection and more. The researchers in the field of deep learning are contributing immensely to bring some fantastic applications in the field. In this article, we list ten deep learning researchers, in no particular order, who are re-defining the application areas of deep learning. A pioneer in deep learning and machine learning-based research, Hinton's work is aimed at finding complex structure in large, high-dimensional datasets, and understanding how the brain learns to see.

This week's furor over FaceApp has largely centered on concerns that its Russian developers might be compelled to share the app's data with the Russian government, much as the Snowden disclosures illustrated the myriad ways in which American companies were compelled to disclose their private user data to the US government. Yet the reality is that this represents a mistaken understanding of just how the modern data trade works today and the simple fact that American universities and companies routinely make their data available to companies all across the world, including in Russia and China. In today's globalized world, data is just as globalized, with national borders no longer restricting the flow of our personal information - trend made worse by the data-hungry world of deep learning. Data brokers have long bought and sold our personal data in a shadowy world of international trade involving our most intimate and private information. The digital era has upended this explicit trade through the interlocking world of passive exchange through analytics services.

If you aim to become deep learning researcher then you should lean things more deeply then just their implementation part. I would advice to start working on some project along with the reading stuff. A good knowledge of these concepts is required for reading research paper. Then learn basic ML stuff and deep learning concept. Doing this course will give you sufficient knowledge about the basic architectures of deep learning.

"The year 2017 has arrived and we humans are still in charge. That reassuring proclamation came from a New Year's editorial in the Chicago Tribune. If you haven't been paying attention to the news about artificial intelligence, and particularly its newest iteration called deep learning, then it's probably time you started. This technology is poised to completely revolutionize just about everything in our lives. Experts say Canadian workers could be in for some major upheaval over the next decade as increasingly intelligent software, robotics and artificial intelligence perform more sophisticated tasks in the economy. Today, machines are able to "think" more like humans than most of us, even the scientists who study it, ever imagined. They are moving into our workplaces, homes, cars, hospitals and schools, and they are making decisions for us. Artificial intelligence has enormous potential for good. But its galloping development has also given rise to fears of massive economic ...

Already made up of some of the top Deep Learning academic researchers around, the team are working on complex Computer Vision problems; the solutions to these problems will contribute to a'game-changing' product. You'll need to be in the top percentile of Deep Learning talent out there, have a hunger to solve complex problems and truly be up for'start-up life. You can be a Deep Learning Researcher or an Engineer but a bit of both would be great. You'll be joining a high-powered team made up of ex-Googlers, MIT, CMU and Stanford PhDs….Sky's the limit. Experience with Deep Learning open source tools (the more the better – Theano, Caffe, Keras)Do you want to work for one of the most exciting start-ups in the world?