Marktechpost

NOVEMBER 16, 2023

A new research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The model, trained on a substantial dataset, performs strongly in categorizing macula-centered 3D volumes into Normal, iAMD, GA, and nAMD stages.

Deep Learning 96

Deep Learning Convolutional Neural Networks Neural Network Categorization 96

Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

Deep Learning 52

Deep Learning Machine Learning Neural Network Natural Language Processing 52

Marktechpost

NOVEMBER 26, 2023

The capacity of a model to use inputs at inference time to modify its behavior without updating its weights to tackle problems that were not present during training is known as in-context learning or ICL. Due to these convincing discoveries, emergent capabilities in massive neural networks have been the subject of study.

Neural Network 128

Neural Network Categorization AI Researcher AI Research 128

Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

Deep Learning 52

Deep Learning Neural Network Algorithm Machine Learning 52

Marktechpost

MARCH 11, 2024

Value functions are a core component of deep reinforcement learning (RL). Value functions, implemented with neural networks, undergo training via mean squared error regression to align with bootstrapped target values. These methods aim to improve robustness and scalability in deep RL.

Neural Network 112

Neural Network Categorization Deep Learning Robotics 112

Analytics Vidhya

JUNE 26, 2023

Introduction Attention models, also known as attention mechanisms, are input processing techniques used in neural networks. They allow the network to focus on different aspects of complex input individually until the entire data set is categorized.

Neural Network 244

Neural Network Categorization Deep Learning NLP 244

Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. A dataset of labeled images is used to train the network, with each image given a particular class or label.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Auto-classification Categorization 52

Marktechpost

DECEMBER 22, 2023

Researchers from Lebanese American University and United Arab Emirates University have collaborated to employ artificial intelligence (AI) successfully through the Scale Conjugate Gradient Neural Network (SCJGNN), providing numerical solutions for the language-based learning model.

Deep Learning 57

Deep Learning Categorization Neural Network Artificial Intelligence 57

Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! How Does Image Classification Work?

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 98

Marktechpost

APRIL 26, 2024

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Alongside topological structure, nodes often possess textual features providing context.

Large Language Models 129

Large Language Models Machine Learning Neural Network Deep Learning 129

ML Review

MARCH 31, 2019

We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3% This is, clearly, an optical illusion — but for the neural network.

Neural Network 52

Neural Network Machine Learning Deep Learning Explainability 52

Towards AI

APRIL 7, 2024

Regression and classification tasks benefit from this kind of learning. Supervised learning can be applied to GIS applications such as species habitat mapping, land cover categorization, and temperature and precipitation prediction. For example, it takes millions of images and runs them through a training algorithm.

Machine Learning 86

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 86

Pickl AI

APRIL 25, 2023

There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. Unsupervised algorithms do not require training a dataset but instead require data to “learn” on its own. What is Deep Learning? What is Robotics?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Natural Language Processing 52

Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Working of Large Language Models (LLMs) Deep neural networks are used in Large language models to produce results based on patterns discovered from training data. What are large language models used for?

Large Language Models 98

Large Language Models LLM Natural Language Processing Deep Learning 98

Viso.ai

FEBRUARY 11, 2024

It is a technique used in computer vision to identify and categorize the main content (objects) in a photo or video. Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. They applied deep CNN over pre-trained ImageNet-1K, with 24.2M

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 52

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

Marktechpost

JULY 17, 2023

The system analyzes visual data before categorizing an object in a photo or video under a predetermined heading. One of the most straightforward computer vision tools, TensorFlow, enables users to create machine learning models for computer vision-related tasks like facial recognition, picture categorization, object identification, and more.

Computer Vision 86

Computer Vision Neural Network Machine Learning Deep Learning 86

Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience.

Auto-complete 240

Auto-complete ChatGPT Large Language Models AI 240

Pickl AI

JULY 28, 2023

Machine Learning algorithms can extract pertinent information from photos and generate precise predictions about the content or objects present using methods like Convolutional Neural Networks (CNNs). Training the Model: The labeled dataset is used to train a Machine Learning model, typically a deep neural network like a CNN.

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 52

Marktechpost

SEPTEMBER 12, 2023

Fine-grained image categorization delves into distinguishing closely related subclasses within a broader category. Modern algorithms for fine-grained image classification frequently rely on convolutional neural networks (CNN) and vision transformers (ViT) as their structural basis.

Convolutional Neural Networks 96

Convolutional Neural Networks Categorization Neural Network Deep Learning 96

Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system.

Software Engineer 96

Software Engineer Machine Learning Neural Network Artificial Intelligence 96

Unite.AI

FEBRUARY 28, 2024

They utilize a transformer-based neural network architecture that excels at processing sequences like text and speech. The sheer scale of these models, combined with advanced deep learning techniques, enables them to achieve state-of-the-art performance on language tasks.

Large Language Models 162

Large Language Models LLM Machine Learning Neural Network 162

PyImageSearch

FEBRUARY 6, 2023

Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. tomato, brinjal, and bottle gourd).

Auto-classification 144

Auto-classification Computer Vision Neural Network Deep Learning 144

Heartbeat

MARCH 8, 2023

Using Deep Learning To Improve the Traditional Machine Learning Performance — by Edwin Maina The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

Convolutional Neural Networks 40

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 40

Mlearning.ai

FEBRUARY 6, 2023

How to Train a CNN Using tf.GradientTape A simple practical example of how to use TensorFlow's GradientTape to train a convolutional neural network. The computed gradients are essential in order to do backpropagation to correct the errors of the neural network to make it gradualy improve.

Neural Network 52

Neural Network Convolutional Neural Networks Categorization Deep Learning 52

IBM Journey to AI blog

AUGUST 28, 2023

Some common techniques include the following: Sentiment analysis : Sentiment analysis categorizes data based on the nature of the opinions expressed in social media content (e.g., It also automates tasks like information extraction and content categorization. positive, negative or neutral).

Convolutional Neural Networks 144

Convolutional Neural Networks Data Mining Categorization Machine Learning 144

ODSC - Open Data Science

APRIL 10, 2023

Deep Learning with PyTorch and TensorFlow part 1 and 2 Dr. Jon Krohn | Chief Data Scientist | Nebula.io Intermediate Machine Learning with scikit-learn: Pandas Interoperability, Categorical Data, Parameter Tuning, and Model Evaluation Thomas J. Jon Krohn | Chief Data Scientist | Nebula.io

Software Engineer 52

Software Engineer Categorization Data Science Machine Learning 52

ODSC - Open Data Science

MARCH 13, 2023

They are experts in machine learning, NLP, deep learning, data engineering, MLOps, and data visualization. Learn more about a few of our ODSC East 2023 instructors, their backgrounds in education, and why they’re fit for imparting their knowledge. Fan has developed a deep understanding and expertise in these subjects.

Data Science 80

Data Science Software Engineer Deep Learning Machine Learning 80

Marktechpost

SEPTEMBER 6, 2023

With the advancements in machine learning and deep learning techniques, there has also been an increase in automation of various dimensions. This approach speeds up neural network training even when new produce varieties are introduced. The researchers used computer vision to facilitate this process.

Computer Vision 68

Computer Vision Automation Neural Network Categorization 68

Pickl AI

AUGUST 1, 2023

The success of a Machine Learning model depends on various factors, including the quality and quantity of the training data, the model architecture, and hyperparameters’ tuning. Types of Machine Learning Model: Machine Learning models can be broadly categorized as: 1.

Machine Learning 52

Machine Learning Neural Network Algorithm Convolutional Neural Networks 52

Heartbeat

FEBRUARY 23, 2023

Curious how to design your deep neural networks’ depth and shape? But instead of just tuning specific hyperparameters, we can also decide how our network is shaped. But instead of just tuning specific hyperparameters, we can also decide how our network is shaped. Well, here’s the guide for you.

Neural Network 52

Neural Network Categorization Deep Learning Data Science 52

Viso.ai

DECEMBER 20, 2023

link] What is an example of supervised learning? Common algorithms and techniques in supervised learning include Neural Networks , Support Vector Machine (SVM), Logistic Regression, Random Forest, or Decision Tree algorithms. A gentle introduction Image Annotation: New Technologies and Tools What Is Deep Learning?

Computer Vision 98

Computer Vision Neural Network Machine Learning Algorithm 98

Heartbeat

MARCH 6, 2023

Keras provides a simple API to build neural networks and uses Tensorflow as the backend. Before feeding these values to a neural network, the pixel values need to range from 0 to 1. Training the Neural Network In this section, you will build a neural network that requires configuring the layers of the model.

ML 52

ML Neural Network Computer Vision Deep Learning 52

Viso.ai

JANUARY 16, 2024

Artificial Neural Networks (ANNs) are the cornerstone of modern artificial intelligence (AI). This enables ANNs to learn and make intelligent decisions based on input data. This diagram showcases how various layers interact in a neural network – source. Case Background: Emotional Perception AI Ltd v.

Neural Network 52

Neural Network Computer Vision Categorization Algorithm 52

The MLOps Blog

MARCH 1, 2024

TL;DR Deep learning models exhibit excellent performance but require high computational resources. Deep learning models continue to dominate the machine-learning landscape. Training and operating the deep learning models is expensive and time-consuming and has a significant impact on the environment.

Deep Learning 59

Deep Learning Neural Network Categorization Machine Learning 59

ODSC - Open Data Science

JULY 26, 2023

Data Preprocessing Data preprocessing involves cleaning and transforming raw data into a format suitable for machine learning algorithms. This step may include handling missing values, outlier detection, feature scaling, encoding categorical variables, and feature engineering.

Machine Learning 52

Machine Learning ML Data Scientist Neural Network 52

NVIDIA Developer

JUNE 2, 2021

Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. In this workshop, you’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents.

Natural Language Processing 40

Natural Language Processing Neural Network Deep Learning BERT 40