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Leaks - Udemy –Build Your own Self Driving Car

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Description Build Your own Self Driving Car Deep Learning, OpenCV, C is an IoT training course focused on self-driving cars published by Yodemi Academy. In this course, you will use various technologies such as Raspberry Pi computer boards, Arduino UNO board, image processing technology, virtual neural networks, machine learning techniques, etc., and are familiar with the use of each of these tools in the world of the Internet of Things. Machine learning and artificial intelligence are two modern technologies that will have many job opportunities in the near future. The development of IoT-based systems has specific and separate steps and processes that you will learn about in all of these processes. Among the most important topics covered in this course are hardware design, initial installation of Raspberry Pi and Arduino boards, establishing communication links between devices and different parts of the car, image processing with OpenCV4, various techniques Machine learning and… pointed out.


10 tips to boost your Kaggle journey

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I started my journey on Kaggle a year ago, straight after a brief acquaintance with the basics of Python and a couple of books on Machine Learning and Deep Learning. I'm still a beginner, though my Kaggle profile turned out to be the most valuable part of my portfolio which landed me on my first job in Data Science just 5 months later. Here I want to share with you a couple of things I've learned from the awesome Kaggle community during this very first year full of hard work. I know there's a bunch of great notebooks claiming to teach you from an absolute beginner, but it's still best to first build some solid foundation of theory and tech behind data science before jumping straight into the competition. There's no need to read the Deep Learning Book from cover to cover, just find some sources Kaggle makes you able to jump straight in the top 30% of literally any competition by just making a copy of the most scoring public work.


Grading AI: The Hits and Misses

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AZEEM AZHAR: Welcome to The Exponential View podcast where multidisciplinary conversations about the near future happen every week. Now, as an entrepreneur, investor, and analyst I've been inside the technology industry for over 20 years. During that time, I've observed that exponentially developing technologies are changing the face of our economies, business models, and culture in unexpected ways. Now, I return to this question every week in my newsletter Exponential View, in this podcast, as well as in my recent book The Exponential Age. So, in today's edition I wanted to look back and forward on one of the key technologies of the exponential age, artificial intelligence. We're about a decade into the current industrial boom in AI and I thought it was time to take a scorecard, look at what we've achieved, and how and perhaps what we didn't on which milestones have surprised us. To help me I called on a great experts Murray Shanahan, a senior research scientist at London's DeepMind, as well as a professor of cognitive robotics at Imperial College in London. Murray works on machine learning, consciousness, the impacts of artificial intelligence. He and I have known each other for a few years and have indeed done a podcast together previously. We appeared as guests on a show hosted by a technology investor. So, my challenge to Murray today was not simply to access the last 10 years of development, but to look forward to the next 10. It's a bold challenge and we did our best to look forward as well as back. MURRAY SHANAHAN: It's very nice to be here.


AI for Trading

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Stocks are shares or ownership certificates of a company, by buying stocks of a company a person becomes a shareholder of the company and gets profits if the stocks price increases and suffers loss if the stock price declines. The stock market is a very risky place as it is very hard to predict the trend or future price of any particular stock. Most people follow basic trends to invest in the stock market without sufficient data and a result they suffer loss.Those that understand AI technology and know how to handle its dangers will have opportunity throughout the early stages of its adoption. One disadvantage of AI-based trading systems is that they can provide models that are poorer than chance. Because methods based on chart patterns and indicators draw their rewards from a distribution with zero mean before transaction charges, traditional technical analysis is an unsuccessful technique of trading.


Machine Deep Learning for Biology with Python and Tensorflow

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TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.


Getting Started With .NET Machine Learning, Deep Learning, and Artificial Intelligence

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ML.NET is an open-source cross-platform machine learning framework for NET developers, with the help of this framework you can create a custom ML model using C # or F #, without leaving the .NET ecosystem. ML.NET becomes another machine learning framework in online or offline scenarios.


Build Our First Convolutional Neural Network

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Welcome to 4th tutorial part! In the previous tutorial, we built Deep Neural Networks using TensorFlow. Today's most practical applications of deep learning are built using programming frameworks, which have many built-in functions you can call. Run the next cell to load the "Cats vs. Dogs" data-set we are going to use: In the previous tutorial, we had built a fully connected Deep Network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.


Review on Few Shot Object Detection

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Deep learning solutions for classification and object detection are state of the art in computer vision and that's not news anymore. Despite the high accuracy and speed of recent SOTA algorithms, there is one big issue: for a good-performing solution, we need a huge amount of data. In addition, the data must be annotated, which requires a lot of manual work. That was the reason for the development of several new paradigms like self-supervised learning and few-shot learning. Recent progress in the few-shot classification helped to significantly improve the performance of "learn to learn" problem in classification, however few-shot object detection (FSOD) has large potential to grow and improve.


How can a board game help AI solve complex mathematics?

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Artificial intelligence is used across myriad disciplines to trawl through troves of data too complex for the human brain – and indeed the average computer – to process, as well as to solve seemingly unsolvable problems. It's posited that these technological super-brains could help us develop medicines and vaccines, solve economic problems, or engineer next-generation technology, among many other helpful applications. But in one of science's most difficult and often abstract fields, the power of the artificial mind is finally starting to prove itself. For the first time, scientists are using machine learning to come up with theories – rather than simply combing through the raw data – in some of the most confounding fields of mathematics. As described in a new study in the journal Nature, researchers from the universities of Sydney and Oxford have been working with AI lab DeepMind, based in London, to apply machine learning to suggest new avenues for inquiry, and to attempt to prove mathematical theorems. These technological super-brains could help us develop medicines and vaccines, solve economic problems, or engineer next-generation technology.


Meta releases PyTorch Live for creating mobile ML demos 'in minutes'

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Meta has announced PyTorch Live, a library of tools designed to make it easy to create on-device mobile ML demos "in minutes". PyTorch Live was unveiled during PyTorch Developer Day and enables anyone to build mobile ML demo apps using JavaScript, the world's most popular programming language. Introducing @PyTorchLive, an easy to use library of tools for creating on-device ML demos on Android and iOS. With Live, you can build a working mobile app ML demo in minutes. While on-device AI demos cannot currently be shared, Meta says that functionality is on the way.