Towards AI

JANUARY 27, 2023

Deep Learning Explained: Perceptron The key concept behind every neural network. A bit of history Beginning Back in 1943, McCulloch and Pitts published a paper entitled A logical calculus of the ideas immanent in nervous activity — known today as the first mathematical model of a neural network. the following way.

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Unite.AI

DECEMBER 1, 2023

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

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Flipboard

JUNE 20, 2023

In this guide , we explain the key terms in the field and why they matter. All of the definitions were written by a human. It imitates how the human brain works using artificial neural networks (explained below), allowing the AI to learn highly complex patterns in data.

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Flipboard

MARCH 27, 2023

torch.compile torch.compile Definition Accelerating DNNs with PyTorch 2.0 torch.compile torch.compile Definition Accelerating DNNs with PyTorch 2.0 torch.compile Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). What’s New in PyTorch 2.0?

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NVIDIA

JANUARY 29, 2025

With expertise on the intersection of autonomous systems and human-centered design, Covert explains the different stages of AI agents from basic conversational interfaces to fully autonomous systems. Time Stamps 5:34 The definition of digital humans and their current state in industries. 10:30 The evolution of AI agents.

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ML Review

MARCH 31, 2019

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3%

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Viso.ai

FEBRUARY 21, 2024

Graph Neural Networks (GNNs) are a type of neural network designed to directly operate on graphs, a data structure consisting of nodes (vertices) and edges connecting them. In this article, we’ll start with a gentle introduction to Graph Neural Networks and follow with a comprehensive technical deep dive.

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Towards AI

SEPTEMBER 10, 2023

This one is definitely one of the most practical and inspiring. So you definitely can trust his expertise in Machine Learning and Deep Learning. So you definitely can trust his expertise in Machine Learning and Deep Learning. Neural Network is a combination of linear functions and activations.

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Unite.AI

SEPTEMBER 15, 2023

In the realm of AI, a persona isn't too different from its traditional definition: it's a representation of a distinct identity or character. In the financial arena, ChatGPT aids by providing insights into investment strategies, explaining financial products, or addressing tax-related queries.

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Marktechpost

MAY 8, 2024

DRL models, such as Deep Q-Networks (DQN), estimate optimal action policies by training neural networks to approximate the maximum expected future rewards. Different definitions of safety exist, from risk reduction to minimizing harm from unwanted outcomes.

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Pickl AI

AUGUST 23, 2024

Summary: This blog post delves into the importance of explainability and interpretability in AI, covering definitions, challenges, techniques, tools, applications, best practices, and future trends. It highlights the significance of transparency and accountability in AI systems across various sectors.

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Towards AI

JULY 22, 2023

So, don’t worry, this is where Explainable AI, also known as XAI, comes in. HEALTHCARE WITH AI: SOURCE: [link] Let’s go through some instances to help you understand why Explainable AI is so important: Imagine a healthcare system in which, instead of speaking with a doctor, you interact with an AI system to assist you with diagnosis.

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Snorkel AI

MARCH 26, 2025

AI judges must be scalable yet cost-effective , unbiased yet adaptable , and reliable yet explainable. An LLM: the neural network that takes in the final prompt and renders verdict. A typical LLM-as-Judge prompt template includes: The task definition : Evaluate the following contract clause for ambiguity.

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NVIDIA

DECEMBER 6, 2023

s focus on perfecting a vehicle’s perception capabilities using multiple deep neural networks, AV 2.0 The company also recently announced LINGO-1, an AI model that allows passengers to use natural language to enhance the learning and explainability of AI driving models. means for the future of self-driving cars. Unlike AV 1.0’s

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AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of relying on predefined, rigid definitions, our approach follows the principle of understanding a set. Its important to note that the learned definitions might differ from common expectations. Instead of relying solely on compressed definitions, we provide the model with a quasi-definition by extension.

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Towards AI

SEPTEMBER 12, 2024

All resources listed in the guide are free, except some online courses and books, which are certainly recommended for a better understanding, but it is definitely possible to become an expert without them, with a little more time spent on online readings, videos, and practice. Read the complete LLM guide here! How Does AI Work?

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How can we explain it in simple terms?

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. These massive amounts of data that exist in every business are waiting to be unleashed to drive insights. What are large language models?

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Towards AI

FEBRUARY 20, 2024

Definition says, machine learning is the ability of computers to learn without explicit programming. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’.

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Heartbeat

FEBRUARY 6, 2023

In the second step, these potential fields are classified and corrected by the neural network model. R-CNN (Regions with Convolutional Neural Networks) and similar two-stage object detection algorithms are the most widely used in this regard. Faster R-CNN uses a Region Proposal Network (RPN) in the object definition step.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. Object detection is no different.

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Marktechpost

OCTOBER 18, 2023

After these accomplishments, other research has examined the advantages of pre-training large molecular graph neural networks for low-data molecular modeling. Due to the lack of big, labeled molecular datasets, these investigations could only use self-supervised approaches like contrastive learning, autoencoders, or denoising tasks.

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PyImageSearch

FEBRUARY 6, 2023

This is the 3rd lesson in our 4-part series on OAK 101 : Introduction to OpenCV AI Kit (OAK) OAK-D: Understanding and Running Neural Network Inference with DepthAI API Training a Custom Image Classification Network for OAK-D (today’s tutorial) OAK 101: Part 4 To learn how to train an image classification network for OAK-D, just keep reading.

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Flipboard

JULY 10, 2023

An autoencoder is an artificial neural network used for unsupervised learning tasks (i.e., Figure 3 illustrates the visualization of the latent space and the process we discussed in the story, which aligns with the technical definition of the encoder and decoder. What Are Autoencoders? Or requires a degree in computer science?

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Mlearning.ai

JULY 23, 2023

I’m going to explain the detail of Text Classification using Naive Bayes algorithm (Naive Bayes Classifier). This is our new sentence: “I love my dog” For humans, this text would definitely be classified as positive because we understand its context and meaning. How it works? However, for a computer, it doesn’t work the same way.

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Flipboard

FEBRUARY 13, 2023

Let us look at the definition of this call step by step. This function takes as input the model definition file (i.e., dnn.blobFromImage function, check out our blog post , which explains this function in detail. In Deep Learning, we need to train Neural Networks. dnn.blobFromImage(cv2.resize(image, here (i.e.,

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Flipboard

MARCH 6, 2023

In this tutorial, we will dive deeper into the definition of the triplet and discuss its mathematical formulation in detail. Furthermore, we will build our Siamese Network model and write our own triplet loss function, which will form the basis for our face recognition application and later be used to train our face recognition application.

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NVIDIA

NOVEMBER 15, 2023

Patrick Lewis “We definitely would have put more thought into the name had we known our work would become so widespread,” Lewis said in an interview from Singapore, where he was sharing his ideas with a regional conference of database developers. Under the hood, LLMs are neural networks, typically measured by how many parameters they contain.

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Mlearning.ai

JANUARY 17, 2024

Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network. Image credit (CC): BrunelloN In an artificial neural network, a node represents a neuron, and a connection between nodes is a synapse, which unidirectionally transports information.

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Bugra Akyildiz

SEPTEMBER 1, 2024

Before getting started, the phrase of “mechanistic interpretability(MI)” refers to the internal workings of neural networks by examining how specific features are represented and processed. This field aims to transform the learned weights of neural networks into human-understandable algorithms.

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PyImageSearch

MARCH 20, 2023

In previous tutorials, we noted that the pyimagesearch folder contains the code for the dataset module ( dataset.py ), the model definition ( model.py ), and the configuration file ( config.py ), which we discussed in detail. In Deep Learning, we need to train Neural Networks. Or has to involve complex mathematics and equations?

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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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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. photo from [link] As new algorithms are added to robotics, computer vision will definitely get better.

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Marktechpost

MARCH 3, 2025

This article provides a technical analysis of the differences between AI Agents and Agentic AI, exploring their definitions, architectures, real-world examples, and roles in multi-agent systems and human-AI collaboration. In many cases, they lack an explicit reasoning process that explains or justifies their actions.

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Ofemwire

MARCH 1, 2024

If you dont get that, let me explain what AI is, like I would do to a fifth grader. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.

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ML @ CMU

JUNE 30, 2023

Problem definition: The robot must explore an unseen environment to find an object of interest from a first-person RGB-D camera and LiDAR-based pose sensor. The robot has access to only a first-person RGB and depth camera and a pose sensor (computed with LiDAR-based SLAM). This task is challenging.

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