AI News

DECEMBER 18, 2023

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular. “There’s a lot of misconceptions, definitely.

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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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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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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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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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Unite.AI

NOVEMBER 8, 2024

It’s not enough to simply identify unhappy customers — we help explain why and offer recommendations for immediate improvement, keeping customers satisfied in the moment. Can you explain how the AI algorithm processes these physiological signals and translates them into actionable insights for retailers?

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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How to Learn Machine Learning

JUNE 9, 2024

The thought of machine learning and AI will definitely pop into your mind when the conversation is about emerging technologies. 2) Explainable AI Explainability AI and interpretable machine learning are the different names of the same things. Explainability AI addresses these challenges of AI/ML solutions. Isn’t it so?

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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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Pickl AI

NOVEMBER 5, 2024

This article explores the definitions of Data Science and AI, their current applications, how they are shaping the future, challenges they present, future trends, and the skills required for careers in these fields. Explainable AI (XAI) is crucial for building trust in automated systems.

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Mlearning.ai

JULY 14, 2023

In the policy-base approach, the policy usually refers to a neural network ) Episode: An episode refers to a sequence of interactions between an agent and an environment, starting from an initial state until reaching a terminal state. The above is the definition of the value function. Trajectory is a subset of Episode.

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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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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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Applied Data Science

MARCH 11, 2022

Gamification There are many definitions for what a game is. Here, we are interested in the formal definition born in economics and used in computer science: In a game, two or more agents, are interacting by performing actions, which give them rewards. Notice the plural: GANs are not one but two neural networks that are playing a game.

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Viso.ai

JANUARY 16, 2024

Auxiliary information can include text descriptions, summaries, definitions, etc., Matching Networks: The algorithm computes embeddings using a support set, and one-shot learns by classifying the query data sample based on which support set embedding is closest to the query embedding – source.

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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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Marek Rei

NOVEMBER 7, 2024

A recent survey paper by Calderon and Reichart [10] found that less than 10% of NLP interpretability papers consider inherently interpretable self-explaining models, with the authors advocating for more research on causal based interpretability methods. But what’s the trade-off with performance?

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Neural networks are powerful for complex tasks, such as image recognition or NLP, but may require more computational resources. Neural networks , while flexible and capable of handling large-scale data, require a lot of data and computing power.

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Pickl AI

SEPTEMBER 17, 2024

This blog aims to introduce you to dimensionality reduction, explain its importance, and explore the common problems it addresses in Data Analysis. Key Terms and Definitions To fully grasp the concepts of dimensionality reduction, it’s essential to understand the key terms and definitions associated with this field.

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Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). 2020) EBM : Explainable Boosting Machine (Nori, et al. Positional embeddings : Added to the patch embeddings to retain positional information. 2019; Lou, et al.

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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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