Pickl AI

SEPTEMBER 5, 2024

Summary: Convolutional Neural Networks (CNNs) are essential deep learning algorithms for analysing visual data. Introduction Neural networks have revolutionised Artificial Intelligence by mimicking the human brai n’s structure to process complex data. What are Convolutional Neural Networks?

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs learn geometric properties on different scales by applying convolutional filters to input data.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Marktechpost

MARCH 10, 2025

Deep neural networks’ seemingly anomalous generalization behaviors, benign overfitting, double descent, and successful overparametrization are neither unique to neural networks nor inherently mysterious. These phenomena can be understood through established frameworks like PAC-Bayes and countable hypothesis bounds.

Deep Learning 48

Deep Learning Convolutional Neural Networks Neural Network Explainability 48

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

Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. In a typical neural network, you flatten your input one vector, take those input values in at once, multiply them by the weights in the first layer, add the bias, and pass the result into a neuron.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 52

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

Prompt Engineer 246

Prompt Engineer Prompt Engineering ChatGPT Convolutional Neural Networks 246

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

AWS Machine Learning Blog

NOVEMBER 26, 2024

We use the following prompt to read this diagram: The steps in this diagram are explained using numbers 1 to 11. Can you explain the diagram using the numbers 1 to 11 and an explanation of what happens at each of those steps? Architects could also use this mechanism to explain the floor plan to customers.

Convolutional Neural Networks 107

Convolutional Neural Networks LLM Metadata Explainability 107

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Neural Network 155

Neural Network Convolutional Neural Networks BERT Deep Learning 155

ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

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.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network Explainability Algorithm 130

Heartbeat

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

Neural Network 59

Neural Network Convolutional Neural Networks Auto-classification Deep Learning 59

Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. Students learn to implement and analyze models like linear models, kernel machines, neural networks, and graphical models.

Machine Learning 120

Machine Learning Python Neural Network Convolutional Neural Networks 120

Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

IBM Journey to AI blog

SEPTEMBER 24, 2024

“AI could lead to more accurate and timely predictions, especially for spotting diseases early,” he explains, “and it could help cut down on carbon footprints and environmental impact by improving how we use energy and resources.” We get tired, lose our focus, or just physically can’t see all that we need to.

Explainability 184

Explainability Computer Vision Convolutional Neural Networks AI 184

Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. According to researchers, this is the reason for developing GAN, an AI-based generative neural network trained using high-resolution radar precipitation fields.

Deep Learning 104

Deep Learning Convolutional Neural Networks Neural Network Artificial Intelligence 104

PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. Object detection is no different. 2015 ; He et al.,

Convolutional Neural Networks 113

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 113

Heartbeat

FEBRUARY 6, 2023

In the second step, these potential fields are classified and corrected by the neural network model. R-CNN (Regions with Convolutional Neural Networks) and similar two-stage object detection algorithms are the most widely used in this regard. In the first step, potential object areas in the image are determined.

Algorithm 90

Algorithm Convolutional Neural Networks Neural Network Computer Vision 90

NYU Center for Data Science

JANUARY 31, 2024

The idea that neural networks undergo distinct developmental phases during training has long been a subject of debate and fascination. Published in the Transactions on Machine Learning Research (TMLR) , Hu’s work offers new insights into the training dynamics of neural networks.

Neural Network 52

Neural Network Convolutional Neural Networks Machine Learning Explainability 52

Mlearning.ai

FEBRUARY 25, 2023

How does the Artificial Neural Network algorithm work? In the same way, artificial neural networks (ANNs) were developed inspired by neurons in the brain. ANN approach is a machine learning algorithm inspired by biological neural networks. Neural networks were trained faster with GPUs.

Neural Network 52

Neural Network Machine Learning Deep Learning Convolutional Neural Networks 52

Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

Neural Network 52

Neural Network Algorithm Convolutional Neural Networks Natural Language Processing 52

NYU Center for Data Science

SEPTEMBER 25, 2024

I interact with animations, and when I explain mathematical concepts, the math pops up in the background. Professor Canziani emphasizes the interactive nature of the course content: “The videos are not just recordings of lectures. It’s very engaging and designed specifically for online consumption.”

Deep Learning 91

Deep Learning Convolutional Neural Networks Neural Network Data Science 91

Viso.ai

FEBRUARY 21, 2024

Graph Neural Networks (GNNs) are a type of neural network designed to directly operate on graphs, a data structure consisting of nodes (vertices) and edges connecting them. In this article, we’ll start with a gentle introduction to Graph Neural Networks and follow with a comprehensive technical deep dive.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Flipboard

JUNE 13, 2023

LOVO makes explainer videos, podcasts, social media content, and e-learning materials easy. Deep convolutional neural network-based image super-resolution is used. This AI-powered technology generates lifelike voices in a tenth of the time and cost of voice talent. It has an easy-to-use, full-featured UI.

AI Tools 121

AI Tools Convolutional Neural Networks Neural Network ChatGPT 121

AWS Machine Learning Blog

DECEMBER 7, 2023

Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section. CNN-QR is a proprietary ML algorithm developed by Amazon for forecasting scalar (one-dimensional) time series using causal Convolutional Neural Networks (CNNs).

Convolutional Neural Networks 131

Convolutional Neural Networks Algorithm Neural Network Data Science 131

Towards AI

MAY 15, 2024

Source: Author/Adobe Firefly) Last week, as I scrolled through my Instagram feed, an animation video popped up in the famous 3Blue1Brown style explaining how Convolution Neural Networks work. First, generate a 2-minute transcript explaining the transformers architecture at a high-level. to explain what concepts.

Convolutional Neural Networks 103

Convolutional Neural Networks Explainability Python Neural Network 103

TransOrg Analytics

FEBRUARY 11, 2025

As AI continues integrating into every aspect of society, the need for Explainable AI (XAI) becomes increasingly important. What is Explainable AI? Explainable AI (XAI) refers to AI systems that provide understandable and interpretable explanations for their decisions. Why is Explainable AI Important?

Explainable AI 52

Explainable AI Explainability Convolutional Neural Networks Neural Network 52

Towards AI

MARCH 21, 2023

Learn how to generate adversaries for convolutional neural networks (CNNs) with this informative article. While many of his friends were doing internships in different companies, Robert started learning about Neural networks, random forests etc. This has inevitably increased the competition for jobs in the market.

Neural Network 98

Neural Network Convolutional Neural Networks ML Automation 98

Artificial Corner

JANUARY 23, 2025

The course will show you how to set up Python, teach you how to print your first “Hello World”, and explain all the core concepts in Python. This is a crash course in Python — when I took this course I had zero knowledge about programming.

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

Mlearning.ai

MAY 11, 2023

Generative Pre-trained Transformer (GPT) Photo by Levart_Photographer on Unsplash Developed by OpenAI, GPT, which stands for “Generative Pre-trained Transformer,” is a neural network that has the ability to generate human-like language, making it an impressive tool for natural language processing (NLP).

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

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

Heartbeat

MAY 2, 2023

Neural Networks Neural networks are a popular deep learning algorithm that are inspired by the structure and function of the human brain. A neural network is a collection of connected neurons, also known as nodes, that are organized in layers. Visibility, reproducibility, and collaboration.

Deep Learning 52

Deep Learning Convolutional Neural Networks Algorithm Neural Network 52

Google Research AI blog

APRIL 25, 2023

Just like the architecture for a neural network, the search space for the perfect burger follows a layerwise pattern, where each layer has several options with different changes to costs and performance. for the convolution layer. This simplified model illustrates a common approach for setting up search spaces.

Neural Network 107

Neural Network Convolutional Neural Networks Algorithm Software Engineer 107

The MLOps Blog

NOVEMBER 14, 2023

Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Interactive visualizations, in particular, have proven helpful for those new to the field.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

Marktechpost

NOVEMBER 20, 2024

Some researchers have introduced multi-architectural modular deep neural networks to reduce false positives in anomaly detection. Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier.

Deep Learning 83

Deep Learning Convolutional Neural Networks Neural Network Algorithm 83