Towards AI

FEBRUARY 16, 2024

Machine learning algorithms, especially neural networks, are effective at identifying complex patterns in chemical data, which can lead to new insights and speed up processes that were previously dependent on traditional, more time-consuming methods. Join thousands of data leaders on the AI newsletter.

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Towards AI

MARCH 29, 2024

It covers a wide range of topics, from optimizing Neural Networks for a particular hardware platform to new fabrication methods for building new hardware for AI applications. In the first one, Robert Dimond from ARM presented their chiplet… Read the full blog for free on Medium. The DATE 2024 conference programme.

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Towards AI

DECEMBER 19, 2023

For example, a deep neural net used for a loan application scorecard might deny a customer, and we will not be able to explain why. This is where an explainable neural network based on generalized additive models with structured interactions (GAMI-Net) comes into the picture. So, without further ado, let’s dive in!

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Mlearning.ai

FEBRUARY 24, 2023

In this blog post, we will explain what PCA is and how it works and present five examples of PCA analysis. This is done by finding the directions (the principal components) that explain the most variance in the data set. The eigenvalues represent the amount of variance explained by the corresponding eigenvector.

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Towards AI

JULY 22, 2023

So, don’t worry, this is where Explainable AI, also known as XAI, comes in. In this blog, we’ll get headlong into XAI theory, expose its nuts and bolts, investigate numerous methodologies, and, of course, offer a variety of relevant and interesting examples that will make the magic of XAI crystal evident! SOURCE: [link] A.

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Towards AI

OCTOBER 8, 2023

This is exactly what this blog post is intended for. We’ll start from neural networks, brush up on deep learning, figure out what the heck is NLP, and eventually, after the not-so-painstaking process, learn about the working of large language models. What are neural networks? Fret no more! What is Machine Learning?

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Towards AI

AUGUST 11, 2023

IMAGE SOURCE: BY AUTHOR INTRODUCTION: GANs, Generative Adversarial networks, let’s first understand what GANs are. So I have already written a blog on Generative AI where we discuss many types of AI, the hype of AI, and why AI is tending nowadays in every sector like Healthcare, Finance, Trading, marketing, and many more.

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Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. One robust use case for XGBoost is integrating it with neural networks to perform a given task. It was envisioned by Thongsuwan et al.,

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NYU Center for Data Science

JULY 7, 2023

This entree is a part of our Meet the Fellow blog series, which introduces and highlights Faculty Fellows who have recently joined CDS Incoming CDS Faculty Fellow, Yoav Wald Meet Yoav Wald, who will join CDS as a Faculty Fellow this fall.

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IBM Journey to AI blog

JANUARY 15, 2024

This technology excels at applying foundation models, which are large neural networks trained on extensive unlabeled data and fine-tuned for various tasks. Learn more about AI can transform government services The post 5 key areas for governments to responsibly deploy generative AI appeared first on IBM Blog.

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Spatial Transformer Networks Explained The central component of the STN is the spatial transformer module. TensorFlow is well-known for its versatility in designing custom layers.

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NYU Center for Data Science

AUGUST 8, 2023

This entry is a part of our Meet the Fellow blog series, which introduces and highlights Faculty Fellows who have recently joined CDS. One recent article, “ A Rainbow in Deep Network Black Boxes ,” addresses the “black box” problem, offering a novel method to understand the internal workings of deep neural networks.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Your AI must be explainable, fair and transparent.

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Mlearning.ai

MARCH 30, 2023

Today in this blog we will learn about transfer learning and how can you implement it using TensorFlow. To train a machine learning model or a neural network that can yield the best results requires what? Let me explain this in simple words. Then Neural Network in CNN is just for the prediction part.

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Towards AI

FEBRUARY 20, 2024

I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., Machine learning(ML) is evolving at a very fast pace. Models […]

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Heartbeat

JANUARY 31, 2023

Non-linear activation functions that you need to know Photo by Ricardo Gomez Angel on Unsplash Activation functions are equations or mathematical formulas that help us determine the output of a neural network. In deep learning, activation functions add non-linearity to the neural networks. Let’s get started!

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Heartbeat

OCTOBER 18, 2023

By leveraging the power of neural networks, deep learning techniques breathe new life into recommendation algorithms, empowering them to handle complex data and surpass the limitations of their predecessors. We pay our contributors, and we don’t sell ads. If you’d like to contribute, head on over to our call for contributors.

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Unite.AI

JULY 25, 2023

For instance, many blogs today feature AI-generated text powered by LLMs (Large Language Modules) like ChatGPT or GPT-4. Functional Approximation Error: This error stems when the models, such as neural networks, fail to capture the true underlying function that has to be learned from the data.

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Heartbeat

OCTOBER 11, 2023

Interpretability and Explainability: Deep neural networks are one machine learning model that can be very complex and difficult to assess. Artificial neural networks are trained in deep learning, a subfield of machine learning, to learn from data and generate predictions or suggestions. What is Deep Learning?

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Chatbots Life

OCTOBER 9, 2023

A weave is composed of a podcast, a newsletter summary, and a blog post, all created by your Tailor A.I. Researchers are using microwave imaging and convolutional neural networks for breast cancer screening with high accuracy in classifying profiles as healthy or diseased. ? We are looking for beta users. And it’s free!

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Heartbeat

MAY 30, 2023

What are Generative Adversarial Networks (GANs)? GANs are a type of neural network design used to develop new data samples that are similar to the training data. This machine learning subset uses artificially generated neural networks to model complex data relationships. Assume you’re teaching your pet new tricks.

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IBM Journey to AI blog

JUNE 14, 2023

With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, you can create pre-trained models for a vast amount of unlabeled data. But that’s all changing thanks to pre-trained, open source foundation models.

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Pickl AI

MAY 24, 2023

To help you understand Python Libraries better, the blog will explain a Python Libraries for Data Science List which you can learn about. Full-stack developers make use of the library of NumPy and hence, need to posses clear and sound knowledge about him Uses: You can make use of the interface explaining images and sound wave.

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Becoming Human

MARCH 16, 2023

Photo by Paulius Andriekus on Unsplash Welcome back to the next part of this Blog Series on Graph Neural Networks! The following section will provide a little introduction to PyTorch Geometric , and then we’ll use this library to construct our very own Graph Neural Network!

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Heartbeat

AUGUST 1, 2023

I stumbled upon a simple question, what was there before neural nets could efficiently be run on the average computer? Surely research in this field yielded results that could be used without requiring neural networks. Features extracted from them could be fed into neural networks for enhanced performance.

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. Recurrent neural networks (RNNs), are a significant deep learning approach used in time series analysis .

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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Photo by Mingwei Lim on Unsplash Recurrent Neural Networks (RNNs) RNNs are a type of neural network that handles sequential data, like handwritten text.

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Heartbeat

NOVEMBER 29, 2023

Computational Graphs: Static vs. Dynamic: The creation and execution of computational graphs are crucial for modeling complex neural networks. It allows developers to define and manipulate the graph on the go, offering flexibility, especially when dealing with variable-length inputs in recurrent neural networks.

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IBM Journey to AI blog

APRIL 18, 2024

” AGI analyzes relevant code, generates a draft function with comments explaining its logic and allows the programmer to review, optimize and integrate it. Connectionist AI (artificial neural networks): This approach is inspired by the structure and function of the human brain.

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Pickl AI

JULY 14, 2023

The following blog will emphasise on what the future of AI looks like in the next 5 years. Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming.

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Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. In a typical neural network, you flatten your input one vector, take those input values in at once, multiply them by the weights in the first layer, add the bias, and pass the result into a neuron.

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Heartbeat

NOVEMBER 27, 2023

Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs. Deep learning automates and improves medical picture analysis.

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Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. > Finished chain.

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Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Heartbeat

JUNE 14, 2023

Introduction Deep neural network classifiers have been shown to be mis-calibrated [1], i.e., their prediction probabilities are not reliable confidence estimates. For example, if a neural network classifies an image as a “dog” with probability p , p cannot be interpreted as the confidence of the network’s predicted class for the image.

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Google Research AI blog

APRIL 25, 2023

Just like the architecture for a neural network, the search space for the perfect burger follows a layerwise pattern, where each layer has several options with different changes to costs and performance. This simplified model illustrates a common approach for setting up search spaces. for the convolution layer.

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Heartbeat

FEBRUARY 7, 2023

Formula to calculate n-gram probabilities; image by Peter Martigny on Feedly Neural Language Models Neural language models make use of artificial neural networks to discover the patterns and structures in a language. One of the most popular language models is the Recurrent Neural Network Language Model (RNNLM).

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