AI Weekly

JUNE 29, 2023

In the News Next DeepMind's Algorithm To Eclipse ChatGPT IN 2016, an AI program called AlphaGo from Google’s DeepMind AI lab made history by defeating a champion player of the board game Go. Powered by pluto.fi Try Pluto for free today] pluto.fi global investment arm, bringing the total capital raised to $165 million.

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Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. More recent depth estimation frameworks deploy convolutional neural network structures to extract depth in a monocular image.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work? Backpropagation: Backpropagation is a crucial step in training deep learning algorithms.

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Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. This means machine learning algorithms are used to analyze and cluster unlabeled datasets by discovering hidden patterns or data groups without the need for human intervention. How Does Image Classification Work?

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AI Weekly

NOVEMBER 23, 2023

forbes.com Applied use cases From Data To Diagnosis: A Deep Learning Approach To Glaucoma Detection When the algorithm is implemented in clinical practice, clinicians collect data such as optic disc photographs, visual fields, and intraocular pressure readings from patients and preprocess the data before applying the algorithm to diagnose glaucoma.

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Marktechpost

SEPTEMBER 1, 2023

Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. ’ The authors concluded that their extensive investigation into deep learning (DL) and machine learning (ML) algorithms in the context of biometric authentication yielded crucial insights.

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Marktechpost

MAY 5, 2024

Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. and evaluating the same.

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Marktechpost

MAY 6, 2024

Developing new methods and algorithms allows for innovations that benefit various industries, including transportation, manufacturing, and healthcare. Existing research includes convolutional neural networks (CNNs) and transformer-based object detection and segmentation architectures.

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Towards AI

APRIL 29, 2024

Understanding Convolutional Neural Network (CNN) — A Guide to Visual Recognition in the AI Era This article will help you understand the application of conventional artificial neural networks to visual recognition problems. The installation instructions are given in respective sections as per the requirements.

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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Marktechpost

FEBRUARY 3, 2024

Traditional convolutional neural networks (CNNs) often struggle to capture global information from high-resolution 3D medical images. One proposed solution is the utilization of depth-wise convolution with larger kernel sizes to capture a wider range of features.

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Marktechpost

APRIL 12, 2024

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers, and Encoder-Decoder architectures, highlighting their unique features, applications, and how they compare against each other.

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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.,

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Marktechpost

APRIL 24, 2024

However, ensuring the understandability of complex algorithms to ornithologists and biologists is essential. The model comprises a Convolutional Neural Network (CNN) backbone, a prototype layer, and a fully connected final layer.

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AI Weekly

MARCH 21, 2024

nature.com Authorship attribution in twitter: a comparative study of machine learning and deep learning approaches This study proposes an AA approach using machine and deep learning algorithms to accurately predict the author of unknown posts on social media platforms.

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Viso.ai

FEBRUARY 11, 2024

The Need for Image Training Datasets To train the image classification algorithms we need image datasets. These datasets contain multiple images similar to those the algorithm will run in real life. The labels provide the Knowledge the algorithm can learn from. Algorithms that won the ImageNet challenge by year – source.

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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.

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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. Howard et al.

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Unite.AI

MAY 31, 2023

A neural network (NN) is a machine learning algorithm that imitates the human brain's structure and operational capabilities to recognize patterns from training data. Lack of Literature Liquid Neural Networks have limited literature on implementation, application, and benefits.

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Marktechpost

OCTOBER 10, 2023

Its applications are used in many fields, such as image and speech recognition for language processing, object detection, and medical imaging diagnostics; finance for algorithmic trading and fraud detection; autonomous vehicles using convolutional neural networks for real-time decision-making; and recommendation systems for personalized content.

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Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? For example, it takes millions of images and runs them through a training algorithm.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section.

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Marktechpost

FEBRUARY 22, 2024

Traditionally, methods like pixel-based classifications struggled against the backdrop of complex environments, leading researchers to turn towards convolutional neural networks (CNNs) and deep learning for solutions.

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Heartbeat

APRIL 4, 2023

Using Deep Learning Algorithms to Perform Image Style Transfer source: Chelsea Troy Artificial Intelligence (AI) has revolutionized the way computer vision and deep learning algorithms create stunning visuals. One of the fascinating applications of AI is Neural Style Transfer (NST).

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Chatbots Life

APRIL 16, 2024

Examples of Generative Models Generative models encompass various algorithms that capture patterns in data to generate realistic new examples. Examples of Discriminative Models Discriminative models encompass a range of algorithms that excel in diverse tasks such as classification and sequence analysis.

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Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. > Finished chain. . >

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Viso.ai

FEBRUARY 25, 2024

Following that, the development of Convolutional Neural Networks (CNNs) was a watershed moment in the field. The introduction of the Super-Resolution Convolutional Neural Network (SRCNN) later demonstrated that deep learning models could outperform traditional image resolution methods.

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Mlearning.ai

MAY 10, 2023

This variability makes it difficult for AI algorithms to generalize from a limited dataset and produce accurate hand drawings across different scenarios. This psychological aspect adds another layer of complexity for AI algorithms, as they must generate hand drawings that satisfy our innate ability to detect inaccuracies.

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IBM Journey to AI blog

MAY 6, 2024

Machine learning and deep neural network models can effectively analyze this data to identify patterns, correlations and relationships, which is particularly useful for understanding a patient’s unique profile. These algorithms can help curate highly personalized advertisements and content tailored to the desired audience.

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Viso.ai

APRIL 11, 2024

In Object Localization (OL), the algorithm identifies and localizes an object in an image. OL algorithms enable precise locating and context understanding of objects within complex visual environments. The algorithm draws bounding boxes around the identified objects. There are various applications of object localization.

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Marktechpost

DECEMBER 1, 2023

Early approaches heavily relied on manually crafted features and machine learning algorithms such as support vector machines and random forests. However, the advent of deep learning, particularly convolutional neural networks (CNNs), revolutionized FER by adeptly capturing intricate spatial patterns in facial expressions.

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NYU Center for Data Science

JANUARY 31, 2024

Michael Hu, during an interview, delved into the motivation and implications of his research into the way neural networks learn. “We Our goal was to segment training into what the algorithm believes are distinct phases of that learning process.” We wanted to create a tool to analyze learning dynamics,” Hu explained.

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Marktechpost

SEPTEMBER 12, 2023

Modern algorithms for fine-grained image classification frequently rely on convolutional neural networks (CNN) and vision transformers (ViT) as their structural basis. Eliminating this background-induced bias for more accurate results is crucial because it can limit the model’s real-world applicability.

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Pickl AI

JULY 28, 2023

In order to teach Machine Learning algorithms and teach the system different visual patterns, large datasets of labeled photos are used in this process. Analyzing pixel data within an image and extracting pertinent characteristics are often carried out utilizing sophisticated algorithms and deep learning approaches.

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NVIDIA

JUNE 15, 2023

Critical to the development of safe and robust self-driving systems, 3D occupancy grid prediction provides information to autonomous vehicle (AV) planning and control stacks using state-of-the-art convolutional neural networks and transformer models , which are enabled by the NVIDIA DRIVE platform.

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Unite.AI

FEBRUARY 21, 2024

LVMs are usually built using advanced neural network architectures , such as Convolutional Neural Networks (CNNs) or transformers, that can efficiently handle pixel data and detect hierarchical patterns. This method enhances adaptation to the target task or domain, requiring parameter access and modification.

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ODSC - Open Data Science

FEBRUARY 13, 2023

The field of data science changes constantly, and some frameworks, tools, and algorithms just can’t get the job done anymore. We will take a gentle, detailed tour through a multilayer fully-connected neural network, backpropagation, and a convolutional neural network.

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