(testing signal)

Tag: neuralnets

Biologically-inspired Neural Networks for Self-Driving Cars

Watch more in the videoDeep Neural Networks And Other ApproachesResearchers are always looking for new ways to build intelligent models. We all know that really deep supervised models work great when we have sufficient data to train them, but one of the hardest things to do is to generalize well and do it efficiently. We can always go deeper, but it has a high computation cost. So as you may already be thinking, there must be another way to make machines intelligent, needing less data or at…… Read more...

What are graph neural networks (GNN)?

Graphs are everywhere around us. Your social network is a graph of people and relations. So is your family. The roads you take to go from point A to point B constitute a graph. The links that connect this webpage to others form a graph. When your employer pays you, your payment goes through a graph of financial institutions.
Basically, anything that is composed of…… Read more...

Deep neural networks: How to define?

The success of artificial intelligence (AI) nowadays is basically due to deep learning (DL) and its related models. DL is a subfield of machine learning (ML) where a set of algorithms try to model high-level data abstractions, making use of several processing layers, where each type of layer has specific purposes.

However, deep neural networks (DNNs), such as deep convolutional neural networks (CNNs), are based on multilayer perceptron (MLP), a class of feed-forward artificial neural network that has been used for quite some time, even before the advent of the first CNN in 1989. Hence, it comes the question: when a model/network is considered “deep” and not “shallow”?… Read more...

Integrating Scikit-learn Machine Learning models into the Microsoft .NET ecosystem using Open Neural Network Exchange (ONNX) format | by Miodrag Cekikj | Sep, 2021

Using the ONNX format for deploying trained Scikit-learn Lead Scoring predictive model into the .NET ecosystem

Photo by Miguel Á. Padriñán from Pexels
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U-Net Image Segmentation with Convolutional Networks

In our age, semantic segmentation on image data is frequently used for computer vision. U-Net is a backbone network that contains convolutional neural networks for masking objects.

🧶U-Net takes its name from its architecture similar to the letter U as seen in the figure. The input images are obtained as a segmented output map at the output.

You can access the basic level information and working architecture of the U-Net network in the article Image Segmentation with U-Net. This article describes the step-by-step coding of the U-Net in the Python programming language.

Step 1: Obtaining the dataset

In this step, if your dataset will be pulled from an existing code, you can load it from the file as follows.… Read more...

🚀Weekly AI News | Falling in love with a chatbot | Quantum and human consciousness | Rethinking artificial neural networks



Continue reading: https://swisscognitive.ch/2021/09/26/%F0%9F%9A%80weekly-ai-news-falling-in-love-with-a-chatbot-quantum-and-human-consciousness-rethinking-artificial-neural-networks/

Source: swisscognitive.ch
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Tired of AI? Let’s talk about CI.

Photo by Josh Riemer on Unsplash

Inspiration: “Using the human brain as a source of inspiration, artificial neural networks (NNs) are massively parallel distributed networks that have the ability to learn and generalize from examples.” [1]

Each NN is composed of neurons, and their organization defines their architecture. The width and depth of NNs define their architecture; this is where “deep learning” originated — by having deep NNs. In the natural language processing (NLP) realm, the GPT-4 architecture is receiving much attention. For computer vision (CV), I’ve always been a fan of the GoogleNet architecture. No architecture is perfect for every situation, which is why there are so many different ones.

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A Breakdown of Deep Learning Frameworks – KDnuggets

What is a Deep Learning Framework?

A deep learning framework is a software package used by researchers and data scientists to design and train deep learning models. The idea with these frameworks is to allow people to train their models without digging into the algorithms underlying deep learning, neural networks, and machine learning.

These frameworks offer building blocks for designing, training, and validating models through a high-level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, MXNet, and others can also use GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training.

Why Use a Deep Learning Framework?

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Fuse Graph Neural Networks with Semantic Reasoning to Produce Complimentary Predictions

Photo by Maxime VALCARCE on Unsplash
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What is Neural Architecture Search? And Why Should You Care?

Neural Architecture Search aims at discovering the best architecture for a neural network for a specific need. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures. This domain represents a set of tools and methods that will test and evaluate a large number of architectures across a search space using a search strategy and select the one that best meets the objectives of a given problem by maximizing a fitness function.

Reference — Neural Architecture Search overview

NAS is a sub-field of AutoML, which encapsulates all processes that automate Machine Learning problems and so Deep Learning ones.

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Implementing Deep Convolutional Neural Networks in C without External Libraries

Reading YUV video in C

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Graph Neural Network (GNN) Architectures for Recommendation Systems

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Feedback Alignment Methods

Backpropagation’s simplicity, efficiency, and high accuracy and convergence rates, make it the de facto algorithm to train neural networks. However, there is evidence that such an algorithm could not be biologically implemented by the human brain [1]. One of the main reasons is that backpropagation requires synaptic symmetry in the forward and backward paths. Since synapses are unidirectional in the brain, feedforward and feedback connections must be physically distinct. This is known as the weight transport problem.

To overcome this limitation, recent studies in learning algorithms have focused on the intersection between neuroscience and machine learning by studying more biologically-plausible algorithms.

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Programming An Intuitive Image Classifier, Part 1

Image classification is one of the hottest fields of machine learning, data science, and AI, and often used to benchmark certain types of AI algorithms — from logistic regression to deep neural networks.

But for now, I want to take your mind away from those hot techniques, and ask ourselves a question: if us humans saw an image of a handwritten character, or a dog or cat, how would our brains intuitively classify different types of images? Below is an example of digits in an image; “2”, “0”, “1” and “9”.

Photo by NordWood Themes on Unsplash

In the example above of digits (or numbers/numerals), how would our brains differentiate between, say, the 1 and 9 at the bottom?

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Rebuild The Chain Rule to Automatic Differentiation

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Speeding up Neural Network Training With Multiple GPUs and Dask

By Jacqueline Nolis, Head of Data Science at Saturn Cloud


The talk this blog post was based on.

A common moment when training a neural network is when you realize the model isn’t training quickly enough on a CPU and you need to switch to using a GPU. A less common, but still important, moment is when you realize that even a large GPU is too slow to train a model and you need further options.

One option is to connect multiple GPUs together across multiple machines so they can work as a unit and train a model more quickly.

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The Creative Side of Vision Transformers

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Neural Network Can Diagnose Covid-19 from Chest X-Rays

  • New study is 98.4% accurate at detecting Covid-19 from X-rays.
  • Researchers trained a convolutional neural network on Kaggle dataset.
  • The hope is that the technology can be used to quickly and effectively identify Covid-19 patients.

As the Covid-19 pandemic continues to evolve, there is a pressing need for a faster diagnostic system. Testing kit shortages, virus mutations, and soaring numbers of cases have overwhelmed health care systems worldwide. Even when a good testing policy is in place, lab testing is arduous, expensive, and time consuming. Cheap antigen tests, which can give results in 30 seconds, are widely available but suffer from low sensitivity; The tests correctly identifying just 75% of Covid-19 cases a week after symptoms start [2].

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Decrease Neural Network Size and Maintain Accuracy: Knowledge Distillation

Some neural networks are too big to use. There is a way to make them smaller but keep their accuracy. Read on to find out how.

Photo by Avery Evans on Unsplash

Practical machine learning is all about tradeoffs. We can get better accuracy from neural networks by making them bigger, but in real life, large neural nets are hard to use. Specifically, the problem arises not in training, but in deployment. Large neural nets can be successfully trained on giant supercomputer clusters, but the problem arises when it comes time to deploy these networks on regular consumer devices. The average person’s computer or phone cannot handle running these large networks.

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GPT-4 Will Have 100 Trillion Parameters — 500x the Size of GPT-3

100 trillion parameters is a lot. To understand just how big that number is, let’s compare it with our brain. The brain has around 80–100 billion neurons (GPT-3’s order of magnitude) and around 100 trillion synapses.

GPT-4 will have as many parameters as the brain has synapses.

The sheer size of such a neural network could entail qualitative leaps from GPT-3 we can only imagine. We may not be able to even test the full potential of the system with current prompting methods.

However, comparing an artificial neural network with the brain is a tricky business. The comparison seems fair but that’s only because we assume artificial neurons are at least loosely based on biological neurons.

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Machine Learning on Graphs, Part 1

Photo by Alina Grubnyak on Unsplash

Collecting basic statistics

In a series of posts, I will provide an overview of several machine learning approaches to learning from graph data. Starting with basic statistics that are used to describe graphs, I will go deeper into the subject by discussing node embeddings, graph kernels, graph signal processing, and eventually graph neural networks. The posts are intended to reflect on my personal experience in academia and industry, including some of my research papers. My main motivation is to present first some basic approaches to machine learning on graphs that should be used before digging into advanced algorithms like graph neural networks.… Read more...

Atomic Neural Networks, the Future of Computing, Quantum Processes and Consciousness — an in-depth…

Photo by Markus Spiske on Unsplash
Emil Rijcken

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Neural Network Pruning 101

All you need to know not to get lost

Hugo Tessier
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A Quick Dive into Deep Learning

Deep learning is a popular and rapidly growing area of machine learning. Deep learning algorithms are a family of machine learning algorithms that use multi-layer artificial neural networks (ANNs) to perform classification tasks. An artificial neural network is a network of artificial neurons, loosely modeled after a network of animal neurons. An artificial neuron takes a series of inputs (here, x₁ through xₙ), usually assigning each input a weight. It sums them, passing the sum through some type of non-linear function. Then it produces an output (here, y).

Diagram of an artificial neuron. Image mine.
Diagram of an artificial neuron. Image mine.

In an artificial neural network, the artificial neurons are very specifically ordered in layers.

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