Unite.AI

MAY 31, 2023

A neural network (NN) is a machine learning algorithm that imitates the human brain's structure and operational capabilities to recognize patterns from training data. Despite being a powerful AI tool, neural networks have certain limitations, such as: They require a substantial amount of labeled training data.

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Analytics Vidhya

JANUARY 23, 2023

Introduction The sigmoid function is a fundamental component of artificial neural networks and is crucial in many machine-learning applications. The sigmoid function is a mathematical function that maps […] The post Why is Sigmoid Function Important in Artificial Neural Networks?

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Analytics Vidhya

AUGUST 31, 2022

Introduction Deep learning is a branch of Machine learning where higher levels of features from the data can be extracted using an Artificial neural network inspired by the working of a neural system in the human body. A neural network is a combination […].

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Analytics Vidhya

JULY 19, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction: We have witnessed many Data Science (both Machine Learning and. The post Artificial Neural Network simplified with 1-D ECG BioMedical Data! appeared first on Analytics Vidhya.

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Analytics Vidhya

FEBRUARY 25, 2023

Though we have traditional machine learning algorithms, deep learning plays an important role in many tasks better than […] The post Introduction to Neural Network: Build your own Network appeared first on Analytics Vidhya.

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Analytics Vidhya

OCTOBER 21, 2021

This article was published as a part of the Data Science Blogathon If you are a machine learning and AI enthusiast, you must have come across the word perceptron. Perceptron is taught in the first chapter of many deep learning courses. So what exactly it is? What is the inspiration behind it? How exactly it […].

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Analytics Vidhya

JANUARY 25, 2024

Introduction Neural networks have revolutionized artificial intelligence and machine learning. These powerful algorithms can solve complex problems by mimicking the human brain’s ability to learn and make decisions.

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Analytics Vidhya

OCTOBER 12, 2023

Introduction Activation functions are the secret sauce behind the remarkable capabilities of neural networks. While this might sound like an intricate technicality, understanding activation functions is crucial for anyone diving into artificial neural networks.

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Analytics Vidhya

AUGUST 23, 2021

This article was published as a part of the Data Science Blogathon Building a simple Machine Learning model using Pytorch from scratch. Image by my great learning Introduction Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network.

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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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Analytics Vidhya

APRIL 22, 2024

Introduction Deep learning is a fascinating field that explores the mysteries of gradients and their impact on neural networks. Solutions like ReLU activation and gradient clipping promise to revolutionize deep learning, unlocking secrets for training success.

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Analytics Vidhya

JULY 22, 2024

Introduction In this article, we dive into the top 10 publications that have transformed artificial intelligence and machine learning. 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.

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Analytics Vidhya

MAY 27, 2023

Introduction Classifying emotions in sentence text using neural networks involves attributing feelings to a piece of text. It can be achieved through techniques like neural networks or lexicon-based methods. Neural networks involve training a model on tagged text data to predict emotions in new text.

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AssemblyAI

MARCH 21, 2023

While AI systems like ChatGPT or Diffusion models for Generative AI have been in the limelight in the past months, Graph Neural Networks (GNN) have been rapidly advancing. What are the actual advantages of Graph Machine Learning? And why do Graph Neural Networks matter in 2023?

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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

JULY 3, 2023

Introduction ONNX, also known as Open Neural Network Exchange, has become widely recognized as a standardized format that facilitates the representation of deep learning models. One of the key advantages of […] The post ONNX Model | Open Neural Network Exchange appeared first on Analytics Vidhya.

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Analytics Vidhya

JULY 16, 2024

Introduction Assume you are engaged in a challenging project, like simulating real-world phenomena or developing an advanced neural network to forecast weather patterns. Tensors are complex mathematical entities that operate behind the scenes and power these sophisticated computations.

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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

Convergence Assurance Techniques for Modern Deep Learning This member-only story is on us. When we talk about neural networks, we often fixate on the architecture how many layers, what activation functions, the number of neurons. Neural networks face a similar journey. Upgrade to access all of Medium.

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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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Unite.AI

MARCH 20, 2025

IntuiCell , a spin-out from Lund University, revealed on March 19, 2025, that they have successfully engineered AI that learns and adapts like biological organisms, potentially rendering current AI paradigms obsolete in many applications. The system's architecture represents a significant departure from standard neural networks.

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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.

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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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Analytics Vidhya

DECEMBER 12, 2022

Introduction In machine learning and deep learning, the amount of data fed to the algorithm is one of the most critical factors affecting the model’s performance. However, in every machine learning or deep learning problem, it is impossible to have enough data to […].

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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. Instead of relying on shrinking transistors, AI employs parallel processing, machine learning , and specialized hardware to enhance performance.

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Towards AI

DECEMBER 18, 2024

Photo by Mahdis Mousavi on Unsplash Do you want to get into machine learning? I have been in the Data field for over 8 years, and Machine Learning is what got me interested then, so I am writing about this! They chase the hype Neural Networks, Transformers, Deep Learning, and, who can forget AI and fall flat.

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Unite.AI

OCTOBER 2, 2024

Machine learning can analyze these datasets yet preparing them for analysis can be time-consuming and cumbersome. This article examines how Microsoft’s TorchGeo facilitates the processing of geospatial data, enhancing accessibility for machine learning experts.

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AI News

OCTOBER 15, 2024

Machine learning (ML) is revolutionising the way businesses operate, driving innovation, and unlocking new possibilities across industries. Machine learning is a subset of artificial intelligence used to develop algorithms and statistical models to enable computers to perform specific tasks without the need for instructions.

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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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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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Analytics Vidhya

JANUARY 12, 2022

Source: Reference 1 Introduction Tensorflow is a popular open-source machine learning framework developed by Google. It is primarily used by machine learning practitioners in research and industry for the training and inference of deep neural networks.

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Unite.AI

NOVEMBER 19, 2024

Today, machine learning and neural networks build on these early ideas. They enable systems to learn from data, adapt, and improve over time. Automated Machine Learning (AutoML): Developing AI models has traditionally required skilled human input for tasks like optimizing architectures and tuning hyperparameters.

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Analytics Vidhya

MARCH 3, 2022

Introduction The Tensorflow framework is an open end-to-end machine learning platform. It’s a symbolic math toolkit that integrates data flow and differentiable programming to handle various tasks related to deep neural network training and inference.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

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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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