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. This blog post will dive deep into the sigmoid function and explore its properties, applications, and implementation in code. Source: Pixabay First, let’s start with the basics.

Machine Learning 304

Machine Learning Analytics IT Deep Learning 304

Analytics Vidhya

NOVEMBER 24, 2023

Introduction Convolutional Neural Networks (CNNs) have been key players in understanding images and patterns, transforming the landscape of deep learning. The journey began with Yan introducing the LeNet architecture, and today, we have a range of CNNs to choose from.

Deep Learning 248

Deep Learning Analytics 248

Analytics Vidhya

SEPTEMBER 12, 2023

Introduction Neuroevolution is a captivating field where AI merges neural networks and evolutionary algorithms to nurture its creative abilities. This article delves into neuroevolution, exploring its mechanics, applications, and significance.

Analytics 301

Analytics IT 301

Analytics Vidhya

DECEMBER 21, 2023

These models stand out in the creative AI art generation, offering a distinct approach from conventional neural networks. Introduction The amalgamation of artificial intelligence (AI) and artistry unveils new avenues in creative digital art, prominently through diffusion models.

Modeling 244

Modeling Analytics Machine Learning 244

Analytics Vidhya

FEBRUARY 24, 2023

Or maybe you’re curious about how to implement a neural network using PyTorch. Or perhaps you want to explore the exciting world of AI and its career opportunities? Introduction Are you interested in learning about Apache Spark and how it has transformed big data processing?

Data Science 236

Data Science Technology Big Data Data Processing 236

Analytics Vidhya

NOVEMBER 13, 2021

This article was published as a part of the Data Science Blogathon In this article, I am going to build neural network models with TensorFlow to solve a classification problem. Let’s explore together that how we can approach a classification problem in Tensorflow.

Data Science 361

Data Science Publishing Modeling Analytics 361

Analytics Vidhya

DECEMBER 28, 2021

Convolutional Neural Networks or convents are a type of deep learning model which we use to approach computer vision-related applications. In this guide, we will explore how […]. The post A Comprehensive Guide on Deep learning for Computer vision appeared first on Analytics Vidhya.

Deep Learning 380

Deep Learning Data Science Publishing Modeling 380

Analytics Vidhya

APRIL 23, 2023

Scribble Diffusion, an open-source AI-powered web app, allows users to convert their hand-drawn sketches into stunning paintings by leveraging advanced neural network structures.

Technology 242

Technology Analytics 242

Analytics Vidhya

MAY 27, 2023

Extensive medical imaging, autonomous driving, and satellite imaging applications are all supported by the encoder-decoder convolutional neural network UNet. We’ll be exploring UNet’s image segmentation process in depth.

Machine Learning 252

Machine Learning Modeling Analytics IT 252

IBM Big Data Hub

MAY 10, 2024

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Explore GPUs on IBM Cloud The post FPGA vs. GPU: Which is better for deep learning? appeared first on IBM Blog.

Deep Learning 111

Deep Learning Cost-Benefit Manufacturing Optimization 111

KDnuggets

DECEMBER 23, 2022

Permutation plays a significant role in making neural networks work as expected and showing whether they provide valid results. Explore how it affects neural network predictions now.

IT 108

IT Machine Learning 108

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. Get ready to be amazed by the impact of AI translation on global communication and explore the possibilities that lie ahead.

Deep Learning 98

Deep Learning Statistics Technology Machine Learning 98

Rocket-Powered Data Science

DECEMBER 16, 2018

As an example of a cold start in supervised learning, we look at neural network models, where the weights on the edges that connect the various nodes in the network layers are not known initially. There are also examples of cold start in supervised machine learning (where you do have class labels on the training data).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

CIO Business Intelligence

JANUARY 26, 2024

There are also efforts to apply algorithmic advances to applied problems in a range of areas, including bioinformatics, networking and systems, search, and information retrieval. Some others include the Institute for Foundations of Machine Learning, Machine Learning Lab, Machine Learning Research Group, and Neural Networks Research Group.

Machine Learning 99

Machine Learning Statistics Recreation/Entertainment Data Science 99

CIO Business Intelligence

MAY 20, 2022

There are also efforts to apply algorithmic advances to applied problems in a range of areas, including bioinformatics, networking and systems, search, and information retrieval. Some others include the Institute for Foundations of Machine Learning, Machine Learning Lab, Machine Learning Research Group, and Neural Networks Research Group.

Machine Learning 143

Machine Learning Statistics Recreation/Entertainment Data Science 143

O'Reilly on Data

MARCH 31, 2020

For example, if engineers are training a neural network, then this data teaches the network to approximate a function that behaves similarly to the pairs they pass through it. The same neural network code trained with seemingly similar datasets of input and output pairs can give entirely different results.

Management 332

Management Machine Learning Experimentation Metrics 332

KDnuggets

AUGUST 1, 2019

Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. This blog explores how to navigate these challenges. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail.

Deep Learning 101

Deep Learning Modeling Management 101

Ontotext

DECEMBER 15, 2023

By exploring the first topic machine-learning , we find 117k Github repos. By exploring the topic nlp , we find 26.7k By exploring the topic llm , we find 3.6k The official (first) repo is tensorflow/tensor2tensor that has topics: machine-learning reinforcement-learning deep-learning machine-translation tpu. uie paddlenlp ).

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

KDnuggets

OCTOBER 4, 2019

In this guide, we will explore a range of research papers that have sought to solve the challenging task of automating neural network design.

Machine Learning 81

Machine Learning 81

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?

Deep Learning 95

Deep Learning Software Risk Testing 95

IBM Big Data Hub

APRIL 18, 2024

A world where computer minds pilot self-driving cars, delve into complex scientific research, provide personalized customer service and even explore the unknown. Researchers are actively exploring artificial consciousness, general problem-solving and common-sense reasoning within machines.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

Cloudera

FEBRUARY 11, 2022

Some of the more common problems include classification, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), regression, clustering, reinforcement learning and anomaly detection. These include customer sentiment analysis, predictive maintenance, vehicle autopilot systems, fraud detection and chatbots.

Machine Learning 93

Machine Learning Sales Data Science Modeling 93

KDnuggets

SEPTEMBER 3, 2019

The quest for recreating cognitive capabilities of the brain in deep neural networks remains one of the elusive goals of AI. Let’s explore some human cognitive skills that are serving as inspiration to a new generation of AI techniques.

Recreation/Entertainment 69

Recreation/Entertainment 69

IBM Big Data Hub

DECEMBER 4, 2023

Advacements in machine learning algorithms, neural networks and the computational power of generative AI, combined with human expertise, intuition and creativity, can unlock new possibilities and achieve levels of innovation that were previously unimaginable.

Experimentation 67

Experimentation Modeling Metrics Machine Learning 67

Sisense

AUGUST 14, 2020

In Augmented Apps , we examine how product teams are exploring AI and Machine Learning to make their products more intuitive and enhance the user experience. . We’ll explore many of these use cases to build an overview of the state of the art in AI components today, starting with innovation in antivirus software.

Machine Learning 98

Machine Learning Visualization Predictive Analytics Testing 98

bridgei2i

APRIL 16, 2021

They talk about the buzzword that used to be AI and elaborate on the science behind Deep Neural Networks. AI today is largely powered by an algorithm called a deep neural network, which is a type of machine learning algorithm. So, is AI really the solution to every problem around us? It’s also called software 2.0

Deep Learning 98

Deep Learning Machine Learning Digital Transformation Software 98

Laminar Security

DECEMBER 13, 2023

In the context of data discovery, ML algorithms can learn from past data exploration experiences to enhance future ones. Machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are commonly used in data classification.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

Smarten

NOVEMBER 20, 2023

The field of AI includes planning and learning, natural language processing (NLP) and machine learning, as well as reasoning and perception, neural networks, economics, mathematics and statistics. Explore our AI and GPT Articles here. Today, systems developed using AI are used in science, research, government, analytics, etc.,

Technology 52

Technology Digital Transformation Software Consulting 52

CIO Business Intelligence

AUGUST 11, 2023

Accenture uses NLP to analyze contracts: The company’s Accenture Legal Intelligent Contract Exploration (ALICE) tool helps the global services firm’s legal organization of 2,800 professionals perform text searches across its million-plus contracts , including searches for contract clauses. Google Cloud Translation.

Unstructured Data 138

Unstructured Data Modeling Machine Learning Data mining 138

Dataiku

MARCH 25, 2024

At their core, M-LLMs consist of versatile neural networks capable of ingesting various data types, thereby gaining insights across different modalities. Additionally, we’ll explore their proficiency in tasks such as generating descriptive captions for images and answering questions about visual content.

Visualization 132

Visualization Modeling Experimentation Testing 132

CIO Business Intelligence

JUNE 17, 2022

At that time, the financial services company was exploring post-quantum cryptography and already had research relationships with academic institutions working on AI research. When IBM started work on its quantum computing research network, Wells Fargo decided to explore the possibilities. “As Known unknowns.

Measurement 113

Measurement Technology Enterprise Optimization 113

IBM Big Data Hub

OCTOBER 18, 2023

“Artificial Intelligence Fundamentals” introduces learners to the history of AI and explores the ways that AI makes predictions, understands language and images, and learns using Deep Learning , neural networks that attempt to simulate the human brain.

Machine Learning 81

Machine Learning Deep Learning Technology Marketing 81

IBM Big Data Hub

DECEMBER 19, 2023

Unsupervised learning is powered by deep learning and neural networks or auto encoders that mimic the way biological neurons signal to each other. Machine learning algorithms can be trained to detect potential attacks on a network in real-time, protecting user information and system functions.

Machine Learning 71

Machine Learning Optimization Unstructured Data Sales 71

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 88

Metrics Machine Learning Risk Statistics 88

Cloudera

FEBRUARY 25, 2021

They need strong data exploration and visualization skills, as well as sufficient data engineering chops to fix the gaps they find in their initial study. Apply modern, deep learning techniques for anomaly detection to identify network intrusions. Train a convolutional neural network using only a few labeled data points.

Deep Learning 82

Deep Learning Machine Learning Forecasting Visualization 82

IBM Big Data Hub

FEBRUARY 14, 2024

Companies also take advantage of ML in smartphone cameras to analyze and enhance photos using image classifiers, detect objects (or faces) in the images, and even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders. Transportation AI informs many transportation systems these days.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

IBM Big Data Hub

DECEMBER 15, 2023

and EU are exploring ways to balance innovation with security and ethical concerns. Keras, an open-source neural network library written in Python, is known for its user-friendliness and modularity, allowing for easy and fast prototyping of deep learning models. Governments like the U.S.

Deep Learning 71

Deep Learning Experimentation Machine Learning Risk 71