Analytics Vidhya

APRIL 15, 2024

Introduction Mastering Graph Neural Networks is an important tool for processing and learning from graph-structured data. This creative method has transformed a number of fields, including drug development, recommendation systems, social network analysis, and more.

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Unite.AI

MAY 2, 2024

As AI technology progresses, the intricacy of neural networks increases, creating a substantial need for more computational power and energy. In response, researchers are delving into a novel integration of two progressive fields: optical neural networks (ONNs) and neuromorphic computing.

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Analytics Vidhya

APRIL 22, 2024

Introduction Deep learning is a fascinating field that explores the mysteries of gradients and their impact on neural networks. Through vivid visualization and […] The post Exploring Vanishing and Exploding Gradients in Neural Networks appeared first on Analytics Vidhya.

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Analytics Vidhya

APRIL 8, 2023

If the order is […] The post Food Delivery Time Prediction with LSTM Neural Network appeared first on Analytics Vidhya. Other examples are Uber Eats, Food Panda, and Deliveroo, which also have similar services. They provide food delivery options.

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Analytics Vidhya

FEBRUARY 6, 2024

Introduction Creating new neural network architectures can be quite time-consuming, especially in real-world workflows where numerous models are trained during the experimentation and design phase. In addition to being wasteful, the traditional method of training every new model from scratch slows down the entire design process.

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Analytics Vidhya

JANUARY 25, 2024

Introduction Neural networks have revolutionized artificial intelligence and machine learning. However, certain problems pose a challenge to neural networks, and one such problem is the XOR problem.

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Unite.AI

OCTOBER 25, 2023

They've crafted a neural network that exhibits a human-like proficiency in language generalization. When pitted against established models, such as those underlying popular chatbots, this new neural network displayed a superior ability to fold newly learned words into its existing lexicon and use them in unfamiliar contexts.

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ML @ CMU

JANUARY 2, 2025

We use a model-free actor-critic approach to learning, with the actor and critic implemented using distinct neural networks. Since computing beliefs about the evolving state requires integrating evidence over time, a network capable of computing belief must possess some form of memory.

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Analytics Vidhya

JANUARY 29, 2024

But, here’s the problem: this encyclopedia is huge and requires significant time and effort […] The post Optimizing Neural Networks: Unveiling the Power of Quantization Techniques appeared first on Analytics Vidhya. Now, this friend has a precise way of doing things, like he has a dictionary in his head.

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Towards AI

MARCH 4, 2025

When we talk about neural networks, we often fixate on the architecture how many layers, what activation functions, the number of neurons. But just as a race cars performance depends on more than its engine, a neural networks success hinges on much more than its basic structure. Neural networks face a similar journey.

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Towards AI

NOVEMBER 11, 2024

This issue is especially common in large language models (LLMs), the neural networks that drive these AI tools. They happen when an AI, like ChatGPT, generates responses that sound real but are actually wrong or misleading. So, sometimes, they drift into fiction.

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Towards AI

FEBRUARY 11, 2025

Source: Scaler In our ongoing journey to decode the inner workings of neural networks, weve explored the fundamental building blocks the perceptron, MLPs, and weve seen how these models harness the power of activation functions to tackle non-linear problems. The answer lies in loss functions.

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Towards AI

FEBRUARY 12, 2025

Backpropagation is the ingenious algorithm that allows neural networks to truly learn from their mistakes. Its the mechanism by which they analyze their errors and adjust their internal parameters (weights and biases) to improve their future performance.

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Marktechpost

DECEMBER 28, 2024

Additionally, current approaches assume a one-to-one mapping between input samples and their corresponding optimized weights, overlooking the stochastic nature of neural network optimization. It uses a hypernetwork, which predicts the parameters of the task-specific network at any given optimization step based on an input condition.

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Flipboard

MARCH 24, 2025

On Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet , the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 by proving that "deep learning" could achieve things conventional AI techniques could not.

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Towards AI

MARCH 7, 2025

Limitations of ANNs: Move to Convolutional Neural Networks This member-only story is on us. The journey from traditional neural networks to convolutional architectures wasnt just a technical evolution it was a fundamental reimagining of how machines should perceive visual information. Author(s): RSD Studio.ai

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Towards AI

JANUARY 20, 2025

Integrating Bayesian Theory, State-Space Dynamics, and Neural Network Structures for Enhanced Probabilistic Forecasting This member-only story is on us. Thats where the Bayesian State-Space Neural Network (BSSNN) offers a novel solution. Upgrade to access all of Medium.

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Unite.AI

MARCH 9, 2025

Companies like Tesla , Nvidia , Google DeepMind , and OpenAI lead this transformation with powerful GPUs, custom AI chips, and large-scale neural networks. Deep learning and neural networks excel when they can process vast amounts of data simultaneously, unlike traditional computers that process tasks sequentially.

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Flipboard

MARCH 3, 2025

The world's first "biological computer" that fuses human brain cells with silicon hardware to form fluid neural networks has been commercially launched, ushering in a new age of AI technology.

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Unite.AI

OCTOBER 27, 2024

The ecosystem has rapidly evolved to support everything from large language models (LLMs) to neural networks, making it easier than ever for developers to integrate AI capabilities into their applications. is its intuitive approach to neural network training and implementation. environments. TensorFlow.js TensorFlow.js

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Flipboard

MARCH 9, 2025

The shoe box-sized device, dubbed CL1, is a notable departure from a conventional computer, and uses human brain cells to run fluid neural networks. For now, the company is selling the device as a way to train "biological AI," meaning neural networks that rely on actual neurons. are biological brains," Kagan told ABC.

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Marktechpost

OCTOBER 31, 2024

Trending ] LLMWare Introduces Model Depot: An Extensive Collection of Small Language Models (SLMs) for Intel PCs The post XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science appeared first on MarkTechPost. Don’t Forget to join our 55k+ ML SubReddit.

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Analytics Vidhya

SEPTEMBER 29, 2023

Introduction In deep learning, optimization algorithms are crucial components that help neural networks learn efficiently and converge to optimal solutions.

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Flipboard

MARCH 21, 2025

Previously, the latest AI model that powers Claude could only rely on data absorbed during its neural network training process, having a "knowledge cutoff" of October 2024. On Thursday, Anthropic introduced web search capabilities for its AI assistant Claude, enabling the assistant to access current information online.

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Analytics Vidhya

APRIL 15, 2024

Introduction Overfitting in ConvNets is a challenge in deep learning and neural networks, where a model learns too much from training data, leading to poor performance on new data. This phenomenon is especially prevalent in complex neural architectures, which can model intricate relationships.

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Unite.AI

NOVEMBER 28, 2024

Graph AI: The Power of Connections Graph AI works with data represented as networks, or graphs. Graph Neural Networks (GNNs) are a subset of AI models that excel at understanding these complex relationships. This makes it possible to spot patterns and gain deep insights.

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Analytics Vidhya

JANUARY 6, 2024

In a groundbreaking development, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have introduced a novel method leveraging artificial intelligence (AI) agents to automate the explanation of intricate neural networks.

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Analytics Vidhya

JULY 4, 2023

Introduction Denoising Autoencoders are neural network models that remove noise from corrupted or noisy data by learning to reconstruct the initial data from its noisy counterpart. We can stack these autoencoders together to form deep networks, increasing their performance.

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Analytics Vidhya

MARCH 31, 2024

Introduction In recent years, Graph Neural Networks (GNNs) have emerged as a potent tool for analyzing and understanding graph-structured data. By leveraging the inherent structure and relationships within graphs, GNNs offer a unique approach to solving a wide range of machine learning tasks.

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Analytics Vidhya

JULY 22, 2024

We’ll take you through a thorough examination of recent advancements in neural networks and algorithms, shedding light on the key ideas behind modern AI. Introduction In this article, we dive into the top 10 publications that have transformed artificial intelligence and machine learning.

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Unite.AI

FEBRUARY 12, 2025

The fast progress in AI technologies like machine learning, neural networks , and Large Language Models (LLMs) is bringing us closer to ASI. Advancements in technologies like neural networks, which are vital for deep learning due to their design inspired by the human brain, are playing an essential role in the development of ASI.

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Analytics Vidhya

SEPTEMBER 12, 2023

Introduction Neuroevolution is a captivating field where AI merges neural networks and evolutionary algorithms to nurture its creative abilities. It’s akin to AI’s artistic or musical journey, allowing it to paint masterpieces and compose symphonies.

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Analytics Vidhya

MAY 29, 2024

Introduction Neural networks are systems designed to mimic the human brain. Many artificial intelligence applications rely on neural networks. They consist of interconnected neurons or nodes. These nodes work together to interpret data and find patterns.

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Unite.AI

DECEMBER 17, 2024

By combining the power of neural networks with the logic of symbolic AI, it could solve some of the reliability problems generative AI faces. It combines two strengths: neural networks that recognize patterns and symbolic AI that uses logic to reason. This is where neurosymbolic AI can help.

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AI News

FEBRUARY 16, 2024

“While a traditional Transformer functions as one large neural network, MoE models are divided into smaller ‘expert’ neural networks,” explained Demis Hassabis, CEO of Google DeepMind. This specialisation massively enhances the model’s efficiency.”

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Analytics Vidhya

MARCH 7, 2025

To this day, I remember coming across recurrent neural networks in our course work. Sequence data excite you initially, but then confusion sets in when differentiating between the multiple architectures. I asked my advisor, “Should I use an LSTM or a GRU for this NLP project?”

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Analytics Vidhya

JUNE 4, 2024

Introduction Kolmogorov-Arnold Networks, also known as KAN, are the latest advancement in neural networks. Based on the Kolgomorov-Arnold representation theorem, they have the potential to be a viable alternative to Multilayer Perceptrons (MLP).

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