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I'm excited to pen down a series of articles where I will break down the basic components that every deep learning enthusiast should know thoroughly.

#MachineLearning #DeepLearning #algorithms #AI "not as a buzzword, but as a piece of #math" #computers #coding #tech #STEM #ArtificialIntelligence ...

The AI Stack, Explained by Professor and Dean, School of Computer Science, Carnegie Mellon University, Andrew Moore | Youtube [14]

But how do neural nets learn? Below, I will show with an example that uses common techniques and principles.

Architecture of the neural networks. (A) The residue probability network, (B) Weight network, and (C) The full network. The residue probability and weight ...

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And that, what you have just seen, is back-propagation, or the key ingredient of pretty much all deep learning processes.

The left and right sides of the figure are the shallow LR model and the deep deep neural network model, and the middle is the combined model.

... neural networks replace machine learning?" on some hipster media website. These media guys always call any shitty linear regression at least artificial ...

These are my notes for chapter 2 of the Deep Learning book. They can also serve as a quick intro to linear algebra for deep learning.

I could not believe the response I got for my previous blog post learning maths for Machine Learning and Deep Learning. There are definitely lots of people ...

With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally ...

Recurrent Neural Networks (updated 2017). Why use recurrent networks at all? And why use a particular Deep Learning recurrent network called Long Short-Term ...

You're curious about deep learning but unsure how to start. Luckily, you have a background in coding and have found great resources.

I have recently completed that and these are the solutions for the Coursera: Neural Networks and Deep learning course by deeplearning.ai Assignment ...

Here's a sketch of the network, taken from the KSH paper* *Thanks to Ilya Sutskever.. The details are explained below. Note that many layers are split into ...

ANNs are processing devices (algorithms or actual hardware) that are modeled on the neuronal structure of the mammalian cerebral cortex but on a much ...

Confusion matrices for eNTERFACE (left) and YouTube (right). Emo3PD: A Brain like Pyramidal Deep Neural Network ...

... neural networks and machine learning. Apparently, the software engineers annotated tens of thousands of portraits that with a wide spectrum of people ...

a, b, A fully connected four-layer (M, N, O and P) neural network of size m-n-o-p (a) can be mapped to multiple blocks of crossbar arrays surrounded by ...

Conventional Neural Network approaches learn representations that don't transfer well across modalities and this paper ...

OGAWA, Tadashi on Twitter: "=> "LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks", arXiv, Jul 14, ...