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Neural Networks


PyTorch for Beginners - Building Neural Networks

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Deep learning and neural networks are big buzzwords of the decade. Neural Networks are based on the elements of the biological nervous system and they try to imitate its behavior. They are composed of small processing units – neurons and weighted connections between them. The weight of the connection simulates a number of neurotransmitters transferred among neurons. Mathematically, we can define Neural Network as a sorted triple (N, C, w), where N is set of neurons, C is set {(i, j) i, j N} whose elements are connections between neurons i and j, and w(i, j) is the weight of the connection between neurons i and j.


MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners

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To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy. This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation.


Regularization -- Part 2

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These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!


Transfer Learning : the time savior

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The whole backdrop of Artificial intelligence and deep learning is to imitate the human brain, and one of the most notable feature of our brain is it's inherent ability to transfer knowledge across tasks. Which in simple terms means using what you have learnt in kindergarten, adding 2 numbers, to solving matrix addition in high school mathematics. The field of machine learning also makes use of such a concept where a well trained model trained with lots and lots of data can add to the accuracy of our model. Here is my code for the transfer learning project I have implemented. I have made use of open cv to capture real time images of the face and use them as training and test datasets.


How to Build Your Own End-to-End Speech Recognition Model in PyTorch

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Deep Learning has changed the game in speech recognition with the introduction of end-to-end models. Deep Learning has changed the game in speech recognition with the introduction of end-to-end models. These models take in audio, and directly output transcriptions. Two of the most popular end-to-end models today are Deep Speech by Baidu, and Listen Attend Spell (LAS) by Google. Both Deep Speech and LAS, are recurrent neural network (RNN) based architectures with different approaches to modeling speech recognition.


A Beginner's Guide To Attention And Memory In Deep Learning

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It might have never occurred to you how you could make sense of what your friend is blabbering at a loud party. There are all kinds of noises in a party; then how come we are perfectly able to carry out a conversation? This question is known widely as the'cocktail party problem'. Most of our cognitive processes can pay attention to only a single activity at a time. In the case of a party house, our capability of directing attention towards one set of words while ignoring other sets of words, which are often overpowering, is still a conundrum.


Great Resources to Start Learning 'Deep Learning for Image Recognition'

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Watching this playlist is an outstanding start to learn the fundamental concepts of deep learning and artificial neural networks. The lectures are deep dive into deep learning models for image classification. The lectures also explain training deep learning models. This specialization contains 5 courses to understand deep learning foundations and apply them (you can audit the courses for free). Deep learning is getting attention from the researchers.


Intel oneDNN 2.0 Deep Neural Network Library Working On More Performance Tuning - Phoronix

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Intel's open-source oneDNN library, which was formerly known as MKL-DNN and DNNL for this deep neural network library now living under the oneAPI umbrella, continues working on some big performance advancements for its 2.0 release. Intel on Thursday released oneDNN 2.0 Beta 7 and with it comes more Intel CPU performance optimizations around convolutional neural networks, binary primitive performance for the broadcast case, BFloat16 and FP32 weights gradient convolutions, INT8 convolutions with 1x1 kernel and spatial strides, and a variety of other specific areas within this deep learning library seeing optimizations. This is also the first release beginning to see initial performance optimizations for Intel's Xe Graphics architecture - benefiting both the likes of Tiger Lake laptops and the DG1 discrete graphics card. OneDNN 2.0 is also adding AArch64 support and other non-x86 processor support and a variety of other improvements.


Simple Image Classification With CNN Using Tensorflow For Beginners

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Image classification is not a hard topic anymore. Tensorflow has all the inbuilt functionalities that take care of the complex mathematics for us. Without knowing the details of the neural network, we can use a neural network now. In today's project, I used a Convolutional Neural Network (CNN) which is an advanced version of the neural network. If you worked with the FashionMNIST dataset that contains shirts, shoe handbags, etc., CNN will figure out important portions of the images.


Deep Learning's Climate Change Problem

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The human brain is an incredibly efficient source of intelligence. Earlier this month, OpenAI announced it had built the biggest AI model in history. This astonishingly large model, known as GPT-3, is an impressive technical achievement. Yet it highlights a troubling and harmful trend in the field of artificial intelligence--one that has not gotten enough mainstream attention. Modern AI models consume a massive amount of energy, and these energy requirements are growing at a breathtaking rate.