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Deep learning has offered noteworthy capabilities and advances in voice recognition, image comprehension, self-driving car, natural language procession, search engine optimization and more. Did you know that despite such promising scope of deep learning, this variant of artificial intelligence garnered huge sensation in the third iteration i.e. the 2000s-present.

With the emergence of GPUs, deep learning could progress beyond its competition on a plethora of benchmarks and real-world applications. Even the computer vision (one of common use cases of deep learning) community was fairly skeptical until AlexNet demolished all its competitors on ImageNet, in 2011.

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-learning #2021

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FREE 2 day online this weekend, where you will Master data science and machine learning from home with help of an industry epxerts. includes a live project to process large volume of data on with

key Highlights of the workshop :
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-learning

The distribution of a variable is a description of the relative numbers of times each possible outcome will occur in a number of trials.

Formally, a distribution can be defined as a normalized measure, and the distribution of a random variable x is the measure P_x on S^' defined by setting
P_x(A^')=P{s in S:x(s) in A^'},

where (S,S,P) is a probability space, (S,S) is a measurable space, and P a measure on S with P(S)=1.

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