Simplilearn

OCTOBER 3, 2022

Graph Neural Network (GNN) is a new model that can be used to analyse graphs. Graphs are robust data structures that contain relationships between objects, and GNNs allow you to explore these relationships in new ways. For example, you can use a GNN to identify which people are most likely to recommend a product on social media.

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Simplilearn

MARCH 7, 2024

Have you ever wondered about the tech lingo behind your phone's assistant or those online ads? Well, it's all Artificial Intelligence (AI)! With 42% of businesses gearing up for AI, you'll soon hear more AI terms. Here’s your cheat sheet to understand the language of AI and stay in the loop with tech trends!

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Smart Data Collective

SEPTEMBER 25, 2023

You’ll discover how machines are evolving to understand and communicate in different languages, the role of neural networks in language learning, and the challenges of translating complex expressions. Additionally, the introduction of neural machine translation models has revolutionized the field.

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Artificial Intelligence 250

Simplilearn

NOVEMBER 8, 2023

With their colossal neural networks encompassing billions of parameters, they possess a remarkable ability to comprehend and generate human-like text. Large language models, such as GPT-3.5, are at the forefront of artificial intelligence innovation. Read More.

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Simplilearn

JULY 8, 2022

A popular misconception is that programming languages are crucial for an AI framework based on neural networks. The results of an AI application are determined by the model architecture rather than the particular language.

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Vision 337

Simplilearn

MAY 18, 2023

The growth and evolution of AI have been remarkable, with advancements in machine learning algorithms, deep neural networks, and natural language processing revolutionizing various industries.

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Simplilearn

JUNE 6, 2023

Deep learning is a subset of machine learning where artificial neural networks understand and learn from large amounts of data. Similarly to how we learn from our day to day experiences, the deep learning algorithm would perform a task repeatedly, every time tweaking it bit by bit to improve the responses and the final outcome.

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Smart Data Collective

MARCH 23, 2022

Here’s how AI can help prevent digital forgery: Training Neural Networks: A series of algorithms that mimic human intelligence to detect underlying data relationships or neural networks can undergo training to recognize forgery in digital transactions and file systems. AI helps make bulk digital signing more secure.

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Simplilearn

AUGUST 16, 2022

A multilayer artificial neuron network is an integral part of deep learning. Not just that, by the end of the lesson you will also learn: Analyze how to regularize and minimize the cost function in a neural network Carry out backpropagation t. Read More.

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Smart Data Collective

DECEMBER 5, 2022

Neural machine translation is the latest development in this sector. Today, we will discuss Neural Machine Translation (NMT) and how it’s a key player in software localization. What is Neural Machine Translation (NMT)? But before we continue our discussion, let’s define Neural Machine Translation (NMT).

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Dataversity

DECEMBER 16, 2020

Deep learning algorithms are based on neural networks, which commonly have millions of parameters that need to be calculated numerous times in […]. Deep learning is the basis for many complex computing tasks, including natural language processing (NLP), computer vision, one-to-one personalized marketing, and big data analysis.

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Simplilearn

NOVEMBER 29, 2021

A perceptron is a neural network unit (an artificial neuron) that does certain computations to detect features or business intelligence in the input data. And this perceptron tutorial will give you an in-depth knowledge of Perceptron and its activation functions. Let’s begin with understanding what is artificial neuron. Read More.

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Simplilearn

JULY 27, 2021

Neural networks are notoriously hard to program because of their high complexity and the multiple components in them. However, with the help of deep learning APIs, you can significantly reduce the requirements to produce, test and deploy neural networks. However, after creating your neural network, how do you know if yo.

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Smart Data Collective

FEBRUARY 3, 2022

Neural Networks. Well, neural networks are in the image of our brain. They are AI algorithms designed to mimic a network of neurons that we have in our brain. The network is composed of different nodes connected to symbolize the biological brain. We’ve always strived to create something in our image.

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Smart Data Collective

JUNE 24, 2022

You can use data analytics to build neural networks to take advantage of Fibonacci retracement. Towards Data Science has a great guide on this, which entails building neural networks to handle them. Use Data Analytics Tools to Create Neural Networks to Spot Fibonacci Retracement Levels.

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Simplilearn

MARCH 31, 2023

This open-source machine learning library is based on Torch and designed to provide greater flexibility and increased speed for deep neural network implementation. Since its inception by the Facebook AI Research (FAIR) team in 2017, PyTorch has become a highly popular and efficient framework to create Deep Learning (DL) model.

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Smart Data Collective

FEBRUARY 23, 2023

Deep Learning is a concept that first arose in 2006, with Geoffrey Hinton’s DNNs (Deep Neural Networks) training concept. Deep learning is achieved when data is run through layers of neural network algorithms. So, can using deep learning to write code help you to stand out as a software developer? What is Deep Learning?

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Simplilearn

SEPTEMBER 1, 2022

It is a way to reduce data dimensions since it is an unsupervised neural network that is trained using unsupervised learning techniques to build a low-dimensional, discretized representation from the input space of the training samples. The concept of a self-organizing map, or SOM, was first put forth by Kohonen. Read More.

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InfoQ Articles

JANUARY 23, 2023

Technology has taken significant leaps within the last few years, introducing advancements that have taken us further into the digital age — impacting the software testing industry and we're seeing advances in machine learning, artificial intelligence, and the neural networks making them possible. By Adam Sandman

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InfoQ Articles

JANUARY 29, 2021

Underlying much of artificial intelligence research is the idea that the essence of an individual resides in the brain. This is contrary to neuroscience which has discovered that a brain cannot work independently from the body and its environment.

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Stickyminds

MARCH 14, 2024

Deep learning, particularly Convolutional Neural Networks (CNNs), has transformed object detection in computer vision. These AI models excel at identifying and pinpointing objects in images and videos with high accuracy.

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Smart Data Collective

JANUARY 14, 2022

For example, research trained neural networks with millions of leaked passwords, resulting in a 26% success rate when generating new passwords. The effectiveness of ransomware depends on how quickly it can spread in a network system. Criminals are also employing artificial intelligence to hack passwords more quickly. Ransomware.

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Fingent

JANUARY 14, 2024

It connects, coordinates, and empowers various departments, as a neural network enables communication and coordination throughout the body. Custom Enterprise Mobile App Development: Why Is It A Business Necessity Today? An Enterprise Mobile Application is the central nervous system of a business.

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My Agile Partner

SEPTEMBER 28, 2023

Neural Networks and Deep Learning: Neural networks, inspired by the human brain, are a subset of machine learning. Deep learning, a subfield of neural networks, has gained immense popularity in recent years. Deep neural networks can process data in layers, allowing for complex decision-making.

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Analysts Corner

SEPTEMBER 25, 2023

Understanding Machine Learning and Artificial Intelligence Data scientists must be well-versed in machine learning algorithms and techniques, including supervised and unsupervised learning, neural networks, and deep learning. This involves mastering tools and libraries such as pandas and NumPy in Python, and dplyr and ggplot2 in R.

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Smart Data Collective

SEPTEMBER 19, 2019

Good synthesizer technology is key to a good TTS system, which requires sophisticated deep learning neural analysis tools. In fact, recorded speech data helps train deep neural networks. it is only a matter of time before we are able to synthesize true-to-life speech for use in our ever-evolving network of things.

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Analysts Corner

MARCH 7, 2023

There are several types of classification algorithms, including logistic regression, decision trees, random forests, support vector machines, and neural networks. This can be used in applications such as fraud detection, network intrusion detection, and quality control. How does classification work?

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Smart Data Collective

NOVEMBER 14, 2019

Neural networks that try to understand relationships between variables when certain independent variables are not well understood. Neural networks could even be used to identify unknown variables and later incorporate them into the model.

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Smart Data Collective

JULY 8, 2021

Artificial Intelligence employs machine learning algorithms such as Deep Learning and neural networks to learn new information like humans. It removes the need for two sets of humans working in day and night shifts who can be employed to carry out other critical tasks. Training and operation cost reduction.

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Smart Data Collective

APRIL 22, 2020

To do this, scientists train neural networks using vast databases of existing DTI data. Their methodology involved a neural network trained on “knowledge graphs” that had previously been successful in finding Baricitinib , one of the earliest examples of a potential treatment candidate for COVID-19.

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Astera

MAY 4, 2023

Deep Learning (DL) Deep Learning is a more advanced form of Machine Learning that leverages artificial neural networks to emulate the way the human brain processes information. Key concepts in DL Artificial Neural Networks (ANN) Artificial Neural Networks are the foundation of Deep Learning.

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Smart Data Collective

JANUARY 27, 2021

You need to start using an LSTM neural network to begin predicting price patterns of the cryptocurrency. The steps are covered below: You need to collect cryptocurrency data in real-time. You need to carefully organize the data so that it can be tested and trained. There are a number of data sources that investors can utilize.

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Smart Data Collective

FEBRUARY 15, 2022

They rely on very complex neural networks to accomplish this. This new service has proven to be remarkably useful. It can detect even the most heavily disguised and modified forms of paraphrasing to text. These tools use deep learning to improve the process constantly.

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Analysts Corner

MAY 21, 2023

Hyperparameters might include the number of leaves or depth in a decision tree, the number of hidden layers in a neural network, the learning rate in gradient descent, among others. They need to be preset, and their correct configuration can significantly affect the learning process and model’s performance.

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ElegantJ BI

SEPTEMBER 23, 2021

Multilayer perceptron (MLP) is a technique of feed-forward artificial neural networks using a back propagation learning method to classify the target variable used for supervised learning. This article describes the analytical technique of multilayer perceptrons for classification. What is a Multilayer Perceptron Classifier?

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Data Discovery Business Intelligence Business Intelligence Data Visualization 130

ElegantJ BI

SEPTEMBER 23, 2021

Multilayer perceptron (MLP) is a technique of feed-forward artificial neural networks using a back propagation learning method to classify the target variable used for supervised learning. This article describes the analytical technique of multilayer perceptrons for classification. What is a Multilayer Perceptron Classifier?

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Data Visualization 130

ElegantJ BI

SEPTEMBER 23, 2021

Multilayer perceptron (MLP) is a technique of feed-forward artificial neural networks using a back propagation learning method to classify the target variable used for supervised learning. This article describes the analytical technique of multilayer perceptrons for classification. What is a Multilayer Perceptron Classifier?

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