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Time-Series Prediction of Bitcoin Price Using LSTM's

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Being a data scientist by profession and a part-time crypto trader by passion, I have been very interested in creating a Deep Learning model that could help me predict Bitcoin price. This article is based on the experimentation I did to create such a model. Long short term memory, or more popularly known as LSTM's, is a type of Recurrent Neural network that helps the model learn long-term sequences in the data set. Since my focus here is more on their usage, if you are interested in knowing more details about what LSTM's are and how they work, you can check out this great article that goes in-depth to explain all that. I imported the data onto my local drive and read it as a CSV using pandas. For this model I created fields to track the hour of the day and the weekday.


The complexity of artificial intelligence

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Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyzes data and makes predictions, and relies heavily on human supervision. SMU Assistant Professor of Information Systems, Sun Qianru, likens training a small-scale AI model to teaching a young kid to recognize objects in his surroundings.


Deep Learning in AI Chips

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Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.


Featurespace Launches Automated Deep Behavioral Networks

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Today, Featurespace introduces Automated Deep Behavioral Networks for the card and payments industry, providing a deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020. "The significance of this development goes beyond the scope of addressing enterprise financial crime. "The significance of this development goes beyond the scope of addressing enterprise financial crime. It's truly the next generation of machine learning," said Dave Excell, founder of Featurespace. A breakthrough in deep learning technology, this invention required an entirely new way to architect and engineer machine learning platforms. Automated Deep Behavioral Networks is a new architecture based on Recurrent Neural Networks that is only available through the latest version of the ARIC Risk Hub. Deep learning technology has various applications, such as in natural language processing for the prediction of the next word in a sentence, however its use in preventing fraud in card and payments fraud detection has not been optimized to protect companies and consumers from card and payments fraud. With this invention, that challenge is solved. Transactions are intermittent, making contextual understanding of time critical to predicting behavior. Previously, building effective machine learning models for fraud prevention required data scientists to have deep domain expertise to identify and select appropriate data features – a laborious, yet vital step. Featurespace Research developed Automated Deep Behavioral Networks to automate feature discovery and introduce memory cells with native understanding of the significance of time in transaction flows, improving upon the market-leading performance of the company's Adaptive Behavioral Analytics. Detecting fraud before the victim's money leaves the account is the best line of defense against scams, account takeover, card and payment fraud attacks. Excell continued, "As real-time payments, digital transformation and consumer demand require the instantaneous movement of money, our role is to ensure the industry has the best tools for protecting their organizations and consumers from financial crime.


Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? - Blog

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Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three words that are often used interchangeably to describe intelligent software. Deep learning is a subset of machine learning, which is a subset of AI. Any computer program that performs smart tasks is referred to as AI. Artificial intelligence (AI) is a technology that allows us to create intelligent systems that can mimic human intelligence. AI can refer to anything from a computer program playing chess, to a voice-recognition system like Alexa. It may be referred to as the output of a computer.


Neural Network 101 - Ultimate Guide for Beginners

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"Imagine you are on a space mission to go to mars as a part of "Project Aries". You are in a spaceship along with your crew (8 in total) along with an ASI(Artificial Super Intelligence) let's called it "HAL9000″. You are drifting through the vast vacuum of the universe millions of miles away from earth. In order to preserve your valuable resources like energy and resources like oxygen and water, you along with your crew enter into a deep sleep state for 4 months. In the meanwhile, your onboard ASI will be monitoring and controlling all operations of your spacecraft.


Global Big Data Conference

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Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.


Multimodal deep learning approach for event detection in sports using Amazon SageMaker

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Have you ever thought about how artificial intelligence could be used to detect events during live sports broadcasts? With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data. Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they're dependent upon the quality and amount of data used in model development. This post explains a deep learning-based approach developed by the Amazon Machine Learning Solutions Lab for sports event detection using Amazon SageMaker. Our solution uses a multimodal architecture utilizing video, static images, audio, and optical flow data to develop and fine-tune a model, followed by boosting and a postprocessing algorithm.


TensorFlow 3D: Deep Learning for Autonomous Cars' 3D Perception

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Google has released TensorFlow 3D, a library that adds 3D deep-learning capabilities to the TensorFlow machine-learning framework. The new library brings tools and resources that allow researchers to develop and deploy 3D scene understanding models. TensorFlow 3D contains state-of-the-art models for 3D deep learning with GPU acceleration. These models have a wide range of applications from 3D object detection (e.g. For instance, 3D object detection is a hard problem using point cloud data due to high sparsity.


Edge#4: Beauty of Neural Architecture Search, and Uber's Ludwig that needs no code

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In this issue: we look at Neural Architecture Search (NAS) and how it relates to AutoML; we explain the research paper “A Survey on Neural Architecture Search” and how it helps to understand NAS; we speak about Uber’s Ludwig toolbox that lowers the entry point for developers by enabling the training and testing of ML models that can be done without writing code.