Mlearning.ai

MARCH 31, 2023

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. The output layer is set to use Softmax Activation Function as usual in Deep Learning classifiers. That time, tensorflow/pytorch and the Deep Learning technology were not ready yet.

Deep Learning 98

Deep Learning Machine Learning Python Categorization 98

Viso.ai

DECEMBER 6, 2023

The most common example is security analytics , where deep learning models analyze CCTV footage to detect theft, traffic violations, or intrusions in real-time. Image Classification Image classification tasks involve CV models categorizing images into user-defined classes for various applications.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Heartbeat

AUGUST 21, 2023

AlexNet was created to categorize photos in the ImageNet dataset, which contains approximately 1 million images divided into 1,000 categories. AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. We pay our contributors, and we don’t sell ads.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 52

Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Uysal and Gunal, 2014). Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. Ensemble deep learning: A review.

BERT 52

BERT NLP Natural Language Processing Algorithm 52

ML Review

MARCH 31, 2019

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. Kurakin et al, ICLR 2017.

Neural Network 52

Neural Network Machine Learning Deep Learning Explainability 52

Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. Text Processing with CNNs In text processing, CNNs are remarkably efficient, particularly in tasks like sentiment analysis, topic categorization, and language translation.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Heartbeat

OCTOBER 9, 2023

Artificial Intelligence (AI) Integration: AI techniques, including machine learning and deep learning, will be combined with computer vision to improve the protection and understanding of cultural assets. International Journal of Heritage in the Digital Era , 1 (1_suppl), 1–6. We pay our contributors, and we don't sell ads.

Computer Vision 52

Computer Vision Algorithm Deep Learning Machine Learning 52

AWS Machine Learning Blog

JANUARY 13, 2023

They were admitted to one of 335 units at 208 hospitals located throughout the US between 2014–2015. We used both numerical and categorical features and grouped all records of each patient to flatten them into a single-record time series. patientunitstayid. unitdischargeoffset. unitdischargestatus. Define the model.

Machine Learning 96

Machine Learning ML Categorization Deep Learning 96

Sebastian Ruder

NOVEMBER 14, 2022

This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. The Deep Learning Indaba 2022 in Tunesia. 92] categorized the languages of the world into six different categories based on the amount of labeled and unlabeled data available in them. Joshi et al. [92] Collier, N., & Elliott, D.

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

Heartbeat

APRIL 18, 2023

Human Action Recognition (HAR) is a process of identifying and categorizing human actions from videos or image sequences. The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. It was introduced in 2014 by a group of researchers (A. Zisserman and K.

Convolutional Neural Networks 52

Convolutional Neural Networks Deep Learning Neural Network Machine Learning 52

Bugra Akyildiz

AUGUST 10, 2024

Articles Quantization in deep learning refers to the process of reducing the precision of the numbers used to represent the model's parameters and activations. Typically, deep learning models use 32-bit floating-point numbers (float32) for computations. million per year in 2014 currency) in Shanghai.

BERT 59

BERT Large Language Models Categorization Deep Learning 59