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...
As larger deep neural networks are trained on the latest and fastest chip technologies, an important challenge remains that bottlenecks performance — and it is not compute power. You can try to calculate a DNN as fast as possible, but there is data — and it has to move. Data pipelines on the chip are expensive and new solutions must be developed to advance capabilities.… Read more...
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...
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...
Using the ONNX format for deploying trained Scikit-learn Lead Scoring predictive model into the .NET ecosystem
While being part of a team working on designing and developing a lead scoring system prototype, I faced the challenge of integrating machine learning models into the target environment built around the Microsoft .NET ecosystem. Technically, I implemented the lead scoring predictive model using the Scikit-learn machine learning built-in algorithm for regression, more precisely Logistic Regression. Considering the phases of initial data analysis, data preprocessing, exploratory data analysis (EDA), and the data preparation for the model building itself, I used the Jupyter Notebook environment powered by Anaconda distribution for Python scientific computing.
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...
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/
… Read more...
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.” 
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.
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?
Organizations can combine GNN reasoning capabilities with classic semantic inferencing in Knowledge Graphs to reach the next level AI and predict any business event based on context at scale.
The ability for machines to reason — not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge — is foundational to Artificial Intelligence. The growing momentum around Neuro-Symbolic AI and the increasing reliance on Graph Analytics demonstrate how important these developments are for the enterprise.
Combining AI’s symbolic knowledge processing with its statistical branch (typified by machine learning) produces the best prescriptive outcomes, delivers total AI, and is swiftly becoming necessary to tackle enterprise scale applications of mission-critical processes like foretelling equipment failure, optimizing healthcare treatment, and maximizing customer relationships.
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.
NAS is a sub-field of AutoML, which encapsulates all processes that automate Machine Learning problems and so Deep Learning ones.
We apply FSRCNN on the Y component of the YUV420 video. Since the HVS has less sensitivity on U and V, these components could be upsampled using simpler interpolation algorithms like Bicubic. Hence in this post, we focus on implementing CNN on C, for upsampling U and V components, we simply repeat existing elements to fill the unknown location. In order to implement the inference of a pre-trained deep CNN in C we need to perform the following steps:
- Read Input Data
- Implement Required Operations (Conv, DeConv, PReLU, and etc.)
- Read Weights of the pre-trained network
Reading YUV video in C
In order to read the YUV file with C, we need to know the dimension of the video (width and height).
Recommendation systems are used to generate a list of recommended items for a given user(s). Recommendations are drawn from the available set of items (e.g., movies, groceries, webpages, research papers, etc.,) and are tailored to individual users, based on:
- user’s preferences (implicit or explicit),
- item features,
- and/or user<->item past interactions.
The quantity and the quality of the user and item data determine the quality of the recommendations. Most of the current state-of-the-art recommender systems use deep learning techniques. For a comprehensive overview of these techniques, please check out Zhang, Shuai, et al’s survey paper from 2019 .
From the above description of recommender systems, one can model the data as a graph: with users and items as the nodes and the edges representing the relation between the nodes.
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 . 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.
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”.
In the example above of digits (or numbers/numerals), how would our brains differentiate between, say, the 1 and 9 at the bottom?
Neural networks are cool. The different machine learning frameworks are even cooler. As you may know, modern neural networks are large formulas with a huge number of variables. Given a problem, these frameworks will help you find a suitable set of parameter values, with a process called “training”. This training process can be done quite efficiently thanks to automatic differentiation, or auto diff. Instead of “behind the scene”, I would say that it influences the whole training process below the scene. It is the underlying fundament of the whole training process.
Auto Diff is often implemented by first understanding the required mathematics and then implementing it from a blank source file.
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.
What’s creativity? The most accredited definition is the following:
“Creativity is the capability of creating novel things”
It is considered one of the most important and irreplaceable peculiarities of humankind. But if this is such a special characteristic, it would be impossible for a neural network to imitate it, isn’t it? Well, not exactly. Today we are facing some extraordinary pinpoints in creating a creative AI with generative models, mainly known as Generative Adversarial Networks (GANs). These are considered by one of the fathers of Deep Learning, Yann Le Cunn, the most important breakthrough of the century in the AI field .
- 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 .
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.
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.
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.
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...
In 1987 Professor Kappen received his PhD in theoretical physics from Rockefeller University in New York. After having conducted research at Philips for two years, he returned to academia in 1989. In a quest to understand human-kind better, he changed his research area to neural networks and machine learning.
Professor Kappen’s research interests lie at the interface between statistical physics, computer science, computational biology, control theory and artificial intelligence. For years, he has argued that computing, memory, and energy consumption will be the main challenges for AI’s future growth. Instead of limiting his research by these challenges, professor Kappen recently published a revolutionary article in Nature titled ‘An atomic Boltzmann machine capable of self-adaption’.
Whether it is in computer vision, natural language processing or image generation, deep neural networks yield the state of the art. However, their cost in term of computational power, memory or energy consumption can be prohibitive, making some of them downright unaffordable for most limited hardware. Yet, many domains would benefit from neural networks, hence the need to reduce their cost while maintaining their performance.
That is the whole point of neural networks compression. This field counts multiple families of methods, such as quantization , factorization , distillation  or, and this will be the focus of this post, pruning.
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).
In an artificial neural network, the artificial neurons are very specifically ordered in layers.