Marktechpost

MARCH 11, 2024

Value functions, implemented with neural networks, undergo training via mean squared error regression to align with bootstrapped target values. However, upscaling value-based RL methods utilizing regression for extensive networks, like high-capacity Transformers, has posed challenges.

Neural Network 128

Neural Network Categorization Deep Learning Robotics 128

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

Mlearning.ai

FEBRUARY 28, 2023

Theoretical Explanations and Practical Examples of Correlation between Categorical and Continuous Values Without any doubt, after obtaining the dataset, giving entire data to any ML model without any data analysis methods such as missing data analysis, outlier analysis, and correlation analysis.

Categorization 52

Categorization Data Analysis Neural Network ML 52

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

MAY 3, 2024

After a quick search, I found an MRI dataset on Kaggle that contains brain MRI scans categorized by tumor type: three groups according to the type of tumor, and a fourth group that includes scans of healthy brains. I also found a notebook with a neural network that can categorize the images with perfect accuracy.

Neural Network 52

Neural Network Categorization Metadata AI 52

Marktechpost

MAY 5, 2024

The LM interpretability approaches discussed are categorized based on two dimensions: localizing inputs or model components for predictions and decoding information within learned representations. They explore methods to decode information in neural network models, especially in natural language processing.

Natural Language Processing 113

Natural Language Processing Categorization Neural Network NLP 113

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 is the task of categorizing and assigning labels to groups of pixels or vectors within an image dependent on particular rules. We will cover the following topics: What Is Image Classification?

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 98

Marktechpost

JANUARY 31, 2024

The inherent opacity of these models has fueled interpretability research, leveraging the unique advantages of artificial neural networks—being observable and deterministic—for empirical scrutiny. Inspired by claims suggesting universality in artificial neural networks, particularly the work by Olah et al.

Large Language Models 137

Large Language Models Neural Network Categorization Automation 137

Marktechpost

APRIL 26, 2024

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Alongside topological structure, nodes often possess textual features providing context. Provide a thorough investigation of the potential of graph structures to address the limitations of LLMs.

Large Language Models 136

Large Language Models Machine Learning Neural Network Deep Learning 136

AssemblyAI

FEBRUARY 16, 2024

Entity Detection spots and categorizes important data in your audio, like names, organizations, addresses, and more.   PII Redaction automatically finds and hides personal details like names and email addresses in transcripts to protect privacy.

Neural Network 147

Neural Network Python Categorization Generative AI 147

Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. Moreover, the performance of modern convolutional neural networks in single-view 3D object reconstruction can be surpassed without explicitly inferring the 3D object structure.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network Explainability Categorization 130

Towards AI

APRIL 7, 2024

Supervised learning can be applied to GIS applications such as species habitat mapping, land cover categorization, and temperature and precipitation prediction. Deep learning multiple– layer artificial neural networks are the basis of deep learning, a subdivision of machine learning (hence the word “deep”).

Machine Learning 99

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 99

Marktechpost

FEBRUARY 22, 2024

In computer vision, convolutional networks acquire a semantic understanding of images through extensive labeling provided by experts, such as delineating object boundaries in datasets like COCO or categorizing images in ImageNet.

Natural Language Processing 129

Natural Language Processing Computer Vision Neural Network Categorization 129

Marktechpost

OCTOBER 3, 2023

This distinction is essential for a variety of uses, such as building playlists for particular objectives, concentration, or relaxation, and even as a first step in language categorization for singing, which is crucial in marketplaces with numerous languages.

Artificial Intelligence 130

Artificial Intelligence Artificial Intelligence Neural Network Categorization 130

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

Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. One robust use case for XGBoost is integrating it with neural networks to perform a given task. It was envisioned by Thongsuwan et al.,

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Marktechpost

NOVEMBER 16, 2023

Utilizing a two-stage convolutional neural network, the model classifies macula-centered 3D volumes from Topcon OCT images into Normal, early/intermediate AMD (iAMD), atrophic (GA), and neovascular (nAMD) stages. The study emphasizes the significance of accurate AMD staging for timely treatment initiation.

Deep Learning 109

Deep Learning Convolutional Neural Networks Neural Network Categorization 109

Marktechpost

JULY 2, 2023

It is known that, similar to the human brain, AI systems employ strategies for analyzing and categorizing images. Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks.

Explainability 98

Explainability Neural Network AI Tools Computer Vision 98

Mlearning.ai

APRIL 17, 2023

fillna(0) # Convert the categorical column to a numeric column data['card_identifier'] = data['card_identifier'].astype('category').cat.as_ordered() Each categorical feature is mapped to an integer value, as XGBoost cannot handle categorical features natively. rolling(window=t).count().reset_index(drop=True).fillna(0)

Neural Network 52

Neural Network Categorization Algorithm Python 52

Unite.AI

NOVEMBER 8, 2023

One-hot encoding is a process by which categorical variables are converted into a binary vector representation where only one bit is “hot” (set to 1) while all others are “cold” (set to 0). Functionality : Each encoder layer has self-attention mechanisms and feed-forward neural networks.

BERT 298

BERT NLP Neural Network Natural Language Processing 298

ODSC - Open Data Science

OCTOBER 24, 2023

It easily handles a mix of categorical, ordinal, and continuous features. Yet, I haven’t seen a practical implementation tested on real data in dimensions higher than 3, combining both numerical and categorical features. All categorical features are jointly encoded using an efficient scheme (“smart encoding”).

Categorization 98

Categorization Data Science Neural Network Algorithm 98

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 109

Convolutional Neural Networks Categorization Neural Network Deep Learning 109

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

Pickl AI

APRIL 25, 2023

There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. It is important to note that Machine Learning has several subsets including neural networks, deep learning, and reinforcement learning. What is Neural Network?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Natural Language Processing 52

Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., How Machine Learning Works?

Machine Learning 98

Machine Learning ML Neural Network Algorithm 98

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 98

Computer Vision Neural Network Machine Learning Deep Learning 98

Marktechpost

DECEMBER 11, 2023

Even logistic regression and neural networks might have incorrect calibrations right out of the box. Additionally, the research team demonstrated that atypicality awareness can enhance performance across several skin-tone subgroups without requiring access to group annotations in a case study that categorizes skin lesions.

Machine Learning 122

Machine Learning Neural Network Categorization AI Researcher 122

Heartbeat

OCTOBER 11, 2023

Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach. Interpretability and Explainability: Deep neural networks are one machine learning model that can be very complex and difficult to assess. What is Deep Learning?

Deep Learning 52

Deep Learning Machine Learning Neural Network Natural Language Processing 52

Flipboard

DECEMBER 27, 2023

Instead, programmers can train a neural network on people-like shapes until it reliably identifies human beings in subsequent video. Then they can train the neural network on more sophisticated patterns, such as people falling over, running, fighting, even arguing, or carrying a knife.

Algorithm 158

Algorithm Neural Network Software Development Automation 158

Marktechpost

OCTOBER 4, 2023

Research focuses on categorizing human facial images by emotions through facial expression recognition (FER) using powerful deep neural networks (DNNs). However, accurately classifying unlearned input, particularly non-face images, remains challenging.

Neural Network 127

Neural Network Categorization AI AI 127

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

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

FEBRUARY 11, 2024

It is a technique used in computer vision to identify and categorize the main content (objects) in a photo or video. 2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks. One of the crucial tasks in today’s AI is the image classification.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 52

Marktechpost

SEPTEMBER 6, 2023

This approach speeds up neural network training even when new produce varieties are introduced. To overcome this problem, researchers from Skoltech and other institutions have devised a new way to distinguish weighted goods at a supermarket. The researchers used computer vision to facilitate this process.

Computer Vision 91

Computer Vision Automation Neural Network Categorization 91

Marktechpost

SEPTEMBER 2, 2023

DINOv2, short for Data-Efficient Image Neural Network Version 2, represents a significant leap in computer vision models. These experts have labored to annotate the dataset meticulously, categorizing it across multiple dimensions. However, what distinguishes FACET is the meticulous annotation of expert human annotators.

Computer Vision 135

Computer Vision Neural Network Categorization AI Researcher 135

Mlearning.ai

JANUARY 30, 2023

For the Neural Network-based Classifier you should create (or find) a balanced dataset who can cover all kinds of variations in hands images (e.g. Neural Network-based Classifier A classifier based on neural network is easier to obtain. Neural Network architecture.

Neural Network 52

Neural Network Categorization Robotics Artificial Intelligence 52

Towards AI

AUGUST 25, 2023

Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system. Include summary statistics of the data, including counts of any discrete or categorical features and the target feature. Classify, predict, detect, translate, etc.

Software Engineer 98

Software Engineer Machine Learning Neural Network Artificial Intelligence 98