Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. It covers topics like image processing, cluster analysis, gradient boosting, and popular libraries like scikit-learn, Spark, and Keras.

Machine Learning 120

Machine Learning Python Neural Network Convolutional Neural Networks 120

Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models.

Deep Learning 137

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 137

Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics.

Neural Network 197

Neural Network Convolutional Neural Networks Robotics Deep Learning 197

AI Weekly

JULY 27, 2023

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation.

Neural Network 204

Neural Network Robotics Natural Language Processing Convolutional Neural Networks 204

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. These algorithms can learn and extract intricate features from input images by using convolutional layers.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

Marktechpost

MARCH 30, 2024

Vision Language Models (VLMs) emerge as a result of a unique integration of Computer Vision (CV) and Natural Language Processing (NLP). These innovations enable Mini-Gemini to process high-resolution images effectively and generate context-rich visual and textual content, setting it apart from existing models.

Artificial Intelligence 138

Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Computer Vision 138

Marktechpost

MARCH 26, 2024

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. Observations indicate diminishing returns with increased model depth, mirroring challenges in deep convolutional neural networks for computer vision.

Convolutional Neural Networks 131

Convolutional Neural Networks Natural Language Processing Computer Vision Neural Network 131

Chatbots Life

APRIL 16, 2024

Variational Autoencoders (VAEs) : VAEs are neural networks that learn the underlying distribution of the input data and generate new data points. Generative Adversarial Networks (GANs) : GANs employ two neural networks : a generator that creates data and a discriminator that checks if it’s real.

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 59

Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

Convolutional Neural Networks 289

Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 289

ODSC - Open Data Science

JULY 18, 2023

Series Introduction The first session will provide you with an introduction to this series, as well as the fundamentals of machine learning and deep learning with a focus on fully connected and convolution neural networks. Sign me up! What’s more, these four sessions are only the start.

Deep Learning 52

Deep Learning Convolutional Neural Networks NLP Neural Network 52

Explosion

JUNE 12, 2023

After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Automation 52

Marktechpost

NOVEMBER 10, 2023

Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information.

Convolutional Neural Networks 109

Convolutional Neural Networks Computer Vision Natural Language Processing Neural Network 109

Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Pickl AI

AUGUST 3, 2023

These algorithms have shown remarkable success in solving a wide range of complex tasks, such as image recognition, natural language processing, speech recognition, and more. At the core of Deep Learning is the artificial neural network (ANN), which is inspired by the structure and function of the human brain.

Deep Learning 52

Deep Learning Machine Learning Algorithm Neural Network 52

Unite.AI

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions.

Large Language Models 162

Large Language Models Natural Language Processing Convolutional Neural Networks Neural Network 162

Mlearning.ai

MAY 11, 2023

ChatGPT is an AI language model that has taken the world by storm since its release in 2020. Indeed, this AI is a powerful natural language processing tool that can be used to generate human-like language, making it an ideal tool for creating chatbots, virtual assistants, and other applications that require natural language interactions.

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). RNNs have been extensively employed for natural language processing tasks such as speech recognition and language translation.

Python 52

Python Convolutional Neural Networks Neural Network Deep Learning 52

Towards AI

APRIL 27, 2023

Gradient descent is widely used in various machine learning models, such as linear regression, logistic regression, and neural networks. Neural Networks: In neural networks, gradient descent is used to train the weights and biases of the network to minimize the error between the predicted and actual outputs.

Machine Learning 85

Machine Learning Neural Network Convolutional Neural Networks Natural Language Processing 85

Viso.ai

MARCH 20, 2024

Foundation models are large-scale neural network architectures that undergo pre-training on vast amounts of unlabeled data through self-supervised learning. Use Cases of Foundation Models Foundation models excel in natural language processing, computer vision, and various other artificial intelligence tasks.

AI Developer 52

AI Developer AI Development Computer Vision BERT 52

Heartbeat

MAY 30, 2023

What are Generative Adversarial Networks (GANs)? GANs are a type of neural network design used to develop new data samples that are similar to the training data. This machine learning subset uses artificially generated neural networks to model complex data relationships. Assume you’re teaching your pet new tricks.

Neural Network 72

Neural Network Convolutional Neural Networks Deep Learning Machine Learning 72

Heartbeat

MARCH 8, 2023

Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates. But instead of just tuning specific hyperparameters, we can also decide how our network is shaped.

Convolutional Neural Networks 40

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 40

Mlearning.ai

JULY 11, 2023

Step 3: Explore Neural Networks and Deep Learning Basics Deep learning is based on neural networks. Start by understanding the basics of neural networks, which are networks of connected nodes that imitate the human brain. Also, learn about common algorithms used in machine learning.

Deep Learning 40

Deep Learning Neural Network Convolutional Neural Networks Computer Vision 40

Mlearning.ai

MARCH 30, 2023

To train a machine learning model or a neural network that can yield the best results requires what? How can we train a neural network without having an ample amount of data, even if you have it can you afford to train a model for months? The process although automatic is very time-consuming and computationally heavy.

Neural Network 52

Neural Network AI Developer AI Development Convolutional Neural Networks 52

Mlearning.ai

MARCH 6, 2023

The general Neural Networks can easily misclassify things by adding even a tiny amount of noise into the original data. Generative Adversarial Networks (GANs) address the above challenge. GANs are a type of deep learning architecture comprising two neural networks: a generator and a discriminator.

Neural Network 52

Neural Network Convolutional Neural Networks Machine Learning Natural Language Processing 52

Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

NVIDIA

OCTOBER 18, 2023

Transforming the AI Landscape Generative AI dramatically improves ease of use by understanding human language prompts to make model changes. Those AI models are more flexible in detecting, segmenting, tracking, searching and even reprogramming — and help outperform traditional convolutional neural network -based models.

Robotics 129

Robotics Generative AI Convolutional Neural Networks AI 129

How to Learn Machine Learning

MAY 14, 2023

When selecting projects, consider tackling problems in different domains, such as natural language processing, computer vision, or recommendation systems. Some popular areas of specialization include natural language processing, computer vision, and reinforcement learning.

Machine Learning 52

Machine Learning Natural Language Processing Convolutional Neural Networks Deep Learning 52

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

Generative AI 118

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118

Viso.ai

DECEMBER 22, 2023

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. These deep learning models are central to the advancement of machine learning and AI, particularly in the realm of image processing.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. Recurrent neural networks (RNNs), are a significant deep learning approach used in time series analysis .

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Python 52

Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks.

Machine Learning 52

Machine Learning Neural Network Algorithm Convolutional Neural Networks 52

PyImageSearch

MARCH 30, 2023

Deep Learning and Neural Networks: Traditional machine learning and AI systems relied on linear or iterative learning methods. However, since the 1980s, researchers have developed “neural network” architectures using node-cluster structures and weighted decision-making strategies.

Computer Vision 56

Computer Vision Machine Learning Neural Network Convolutional Neural Networks 56

TensorFlow

MARCH 29, 2023

This plug-in enables neural network inferencing on AMD EPYC CPUs with the AMD ZenDNN library. ZenDNN ZenDNN, which is available open-source from GitHub , is a low-level AMD deep neural network library that includes basic neural network building blocks optimized for AMD EPYC CPUs.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Computer Vision 52

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Viso.ai

DECEMBER 20, 2023

Common algorithms and techniques in supervised learning include Neural Networks , Support Vector Machine (SVM), Logistic Regression, Random Forest, or Decision Tree algorithms. How supervised machine learning works Supervised machine learning is the process of training a model to learn from labelled training data.

Computer Vision 98

Computer Vision Neural Network Machine Learning Algorithm 98