Trade Forex differently… using a learning algorithm designed by expert traders. Just over four years ago, we embarked on an ambitious task on the banks of the Thames in London. We decided to rewrite the rules on FOREX trading. Granted there's a lot to choose from, but for the individual who just wants to trade FOREX profitably without having to be glued to their screen all day, we feel we have developed a viable alternative. Our intuitive approach pushes all the technical analysis onto an AI and ML solution called RagingFX.
We recommend these YouTube channels regardless of your machine learning experience, whether you have a computer science degree or just a passing interest in AI. You'll soon be on the way toward mastering the basics of AI, machine learning, and computer science in no time, through easy-to-follow demos and tutorial videos. The official Deep Learning AI YouTube channel has video tutorials from the deep learning specialization on Coursera. Artificial Intelligence -- All in One: This YouTube channel has tutorial videos related to science, technology, and artificial intelligence. Andrew Ng: Andrew Ng is a computer scientist and entrepreneur, co-founder of Google Brain, former VP & Chief Scientist at Baidu, adjunct professor at Stanford University.
Artificial intelligence technologies are being used across industries to automate and improve the efficacy of different activities. The advanced machine learning systems are equipped to replicate the discreet thinking and analysis patterns demonstrated by humans. This allows companies to leverage AI in performing some of the complex cognitive tasks with minimal human intervention. While AI can be applied to varied use-cases, social media is one segment where it has become a major catalyst for companies to grow. For example, AI chatbots for business are helping companies to stay connected with their audience.
As we know, PyTorch is a popular, open source ML framework and an optimized tensor library developed by researchers at Facebook AI, used widely in deep learning and AI Research. The torch package contains data structures for multi-dimensional tensors (N-dimensional arrays) and mathematical operations over these are defined. In this blog post, we seek to cover some of the useful functions that the torch package provides for tensor manipulation, by looking at working examples for each and an example when the function doesn't work as expected. This function concatenates the given sequence of tensors along the given dimension. All tensors must either have the same shape (except in the concatenating dimension) or be empty.
Google uses common AI tools known as neural networks for a huge variety of tasks, from suggesting text in your Gmail account to serving you up an endless stream of recommended videos every time you fire up the YouTube app. Now, Google has tasked a custom neural net to organize and sync more than 150,000 YouTube cover versions of "Bad Guy," by Billie Eilish. It doesn't sound like all that impressive a task until you consider the scale of the project. From there, however, you can click on the related videos next to the player or any of the hashtags scrolling along the bottom of the screen. Once you do so, the video will seamlessly transition to a cover version of the song that's perfectly synchronized in tempo and key. It works on the most recent versions of all the major browsers on computers, smartphones, and tablets.
YouTube chapters can help you quickly navigate a video, but you often don't have that luxury when creators have to add them by hand. There might not be as much of a rush going forward. The 9to5Google team reports that YouTube is testing automatic, AI-generated video chapters, A machine learning system creates the chapters by looking for text. In other words, a producer who's been thoughtful enough to title sections in the video itself might not have to add chapters later. The internet giant is currently experimenting with auto chapters for a "small group of videos," and is giving creators chances to opt out and offer feedback.
The graph represents a network of 1,100 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 November 2020 at 12:00 UTC. The requested start date was Friday, 20 November 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 16-hour, 59-minute period from Tuesday, 17 November 2020 at 07:37 UTC to Friday, 20 November 2020 at 00:37 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
The graph represents a network of 1,033 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 November 2020 at 12:20 UTC. The requested start date was Friday, 20 November 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 17-hour, 40-minute period from Tuesday, 17 November 2020 at 06:56 UTC to Friday, 20 November 2020 at 00:37 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Earlier this month, YouTube said it would remove videos containing misinformation about COVID-19 vaccines and would expand its current rules against falsehoods and conspiracy theories about the Pandemic. It also revealed it's removed over 200,000 videos containing dangerous or misleading COVID-19 information since early February. No wonder the World Health Organisation says the world isn't just fighting a pandemic, but an'infodemic' as well. As The New York Times recently put it, we are facing "the mass distortion of truth and overwhelming waves of speech from extremists that smear and distract". The problem, allege citizen data scientists: the infodemic isn't just crazy people talking to each other online, which in 2020 is basically BAU.
Artificial Intelligence is a critical tool to help protect people from harmful content. It helps us scale the work of human experts, and proactively take action, before a problematic post or comment has a chance to harm people. Facebook has implemented a range of policies and products to deal with misinformation on our platform. These include adding warnings and more context to content rated by third-party fact-checkers, reducing their distribution, and removing misinformation that may contribute to imminent harm. But to scale these efforts, we need to quickly spot new posts that may contain false claims and send them to independent fact-checkers -- and then work to automatically catch new iterations, so fact-checkers can focus their time and expertise fact-checking new content.