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Is Facebook Building An Autonomous Car?

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Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles. Potential car buyers spend anywhere between 30 to 50 minutes every day on Facebook and that has helped the social business make significant inroads in digital prospecting and omni-channel commerce. Facebook believes that car companies are focusing more on the connected car, rather than the connected consumer. With every new customer car buying journey now beginning online, it is possible through Facebook's huge data on a customer's social behavior, to make that experience personalized and completely customized.


Deep Learning – what is it? Why does it matter?

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This is why in the image you can see that both models result in some errors with reds in the blue zone and blues in the red zone. The theory is that the more hidden layers you have the more you can isolate specific regions of data to classify things. GPU based processing allows for parallel execution, on large numbers of relatively cheap processors, especially when training an artificial neural network with many hidden layers and a lot of input data. That means having them able to understand images, understand speech, understand text etc.


The subtle, invisible AI that big Indian start-ups are using to get to know consumers better

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So, ShopClues plans to use advanced technologies to make it easier for shoppers to find the right size when buying clothes online, according to Utkarsh Biradar, vice-president of product at the company. It's also applying these technologies to help advertisers expand their reach effectively, using machine learning to identify "lookalike" targets that are similar to existing users as well as figuring out what kinds of ads users don't want to see. Ola, one of India's leading ride-hailing apps, is using data science and machine learning to track traffic, improve customer experience, understand driver habits and extend the life of a vehicle. Machine learning models log each customer's gender, brand affinity, store affinity, price preference, frequency, volume of purchases, and more, which become more accurate as the company collects more data.


The subtle, invisible AI that Indian unicorns have made a part of consumers' lives

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So, ShopClues plans to use advanced technologies to make it easier for shoppers to find the right size when buying clothes online, according to Utkarsh Biradar, vice-president of product at the company. It's also applying these technologies to help advertisers expand their reach effectively, using machine learning to identify "lookalike" targets that are similar to existing users as well as figuring out what kinds of ads users don't want to see. Ola, one of India's leading ride-hailing apps, is using data science and machine learning to track traffic, improve customer experience, understand driver habits and extend the life of a vehicle. Machine learning models log each customer's gender, brand affinity, store affinity, price preference, frequency, volume of purchases, and more, which become more accurate as the company collects more data.


What is Deep Machine Learning?

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In my last LinkedIn post I spoke of Deep Learning in and as it relates to Web Development (you can read that particular blog post here Deep learning and Web development) today however I want to take a look at deep machine learning. Perhaps one of the most important things to appreciate about multiple layers of representation is that it's overcome much of the previous issues faced when computer scientists modelled neuron networks and today instead of simply classifying data they can instead generate the data models for themselves. Today deep machine learning features end-to-end learning that can allow a computer to learn free from intermediaries and significant human expertise. And as a perfect example of this is the way in which speech recognition advanced with deep learning free from the previously cumbersome (yet necessary) phonetic representations.