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Deep Learning

Introduction   This blog introduces an interesting application of conditional generative adversarial network (cGAN) for face aging. That is, you can use this cGAN to synthesize the face images of one person at different ages. For research area, this method can be used to improve the performance of “cross-age facial recognition”. For daily application, except for entertainment, it can also be used for finding missing children. This blog...

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If the basic technical ideas behind Deep Learning are around for decades, why are they taking off today ?   The best thing to answer this question would be to show and explain you the picture below.     At the vertical axes of the diagram you can see the performance of an algorithm (e.g. it’s prediction accuracy) and at the horizontal axes you can see the amount of data...

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Today we will start our journey to the world of Artificial Intelligence(AI). We will learn the basic definition of Artificial Intelligence (AI), Machine Learning(ML), Deep Learning(DL), Natural Language Processing(NLP), Computer Vision and Image Processing. Later we will go deeper with the machine learning algorithms and how those algorithm works. This tutorial is for beginners, if you have an idea of AI skip this course and...

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As automakers rev up their engines in the race for self-driving cars, all routes to the finish line run through artificial intelligence. But just as automakers have come to accept that succeeding in the 21st century requires adopting new paradigms, AI itself must undergo major transformations in order to fulfill its promise of making our cars smarter, our roads safer, and our future autonomous. Achieving autonomy will...

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What is machine learning?   Here’s a basic definition of machine learning: “Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions” An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences...

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Do we need to study traditional computer vision since deep learning can handle anything more efficiently?   These are good questions. Deep learning (DL) has certainly revolutionized computer vision (CV) and artificial intelligence in general. So many problems that once seemed improbable to be solved are solved to a point where machines are obtaining better results than humans. Image classification is probably the prime example of this....

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In these times, computer vision is growing as never before. Many things can be mentioned as a reason, but in my view the main reasons are the following: Advancements in hardware The emergence of deep learning The advent of large datasets The increase in computer vision applications   Better and More Dedicated Hardware One of the main reasons why image processing is such a difficult problem is that...

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There are many many deep learning models out there doing various things. Depending on the exact task they are solving, they may be made differently. Some uses convolution followed by pooling. Some uses several convolutional layers before there is any pooling layer. Some uses max-pooling. Some uses mean-pooling. Some have a dropout added. Some have a batch-norm layer here and there. Some uses sigmoid neurons,...

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