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

Explosion

FEBRUARY 12, 2017

In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. The SNLI dataset is over 100x larger than previous similar resources, allowing current deep-learning models to be applied to the problem. This gives us two 2d arrays — one per sentence.

Neural Network 40

Neural Network Deep Learning Natural Language Processing NLP 40

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. 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

Mlearning.ai

MAY 8, 2023

A comprehensive step-by-step guide with data analysis, deep learning, and regularization techniques Introduction In this article, we will use different deep-learning TensorFlow neural networks to evaluate their performances in detecting whether cell nuclei mass from breast imaging is malignant or benign.

Neural Network 52

Neural Network Deep Learning Machine Learning Categorization 52

Viso.ai

JANUARY 16, 2024

This ANN’s training involves understanding and categorizing music based on human perceptions and emotions. Emotional Perception AI Ltd argues that this is going a step beyond conventional categorization. However, machine learning systems evolve and adapt their functions autonomously.

Neural Network 52

Neural Network Computer Vision Categorization Algorithm 52

ML Review

MARCH 31, 2019

In 2017, another group demonstrated that it’s possible for these adversarial examples to generalize to the real world by showing that when printed out, an adversarially constructed image will continue to fool neural networks under different lighting and orientations: Source: Adversarial Examples in the Physical World. Sharif et al.

Neural Network 52

Neural Network Machine Learning Deep Learning Explainability 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. In 2017, the landmark paper “ Attention is all you need ” was published, which laid out a new deep learning architecture based on the transformer.

ML 94

ML Deep Learning Algorithm Large Language Models 94

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. Gonzalez and Richard E. We're committed to supporting and inspiring developers and engineers from all walks of life.

Computer Vision 52

Computer Vision Algorithm Deep Learning Machine Learning 52

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. In Proceedings of NIPS 2017. Shazeer, N.,

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

Topbots

APRIL 11, 2023

LaMDA is built on Transformer , a neural network architecture that Google Research invented and open-sourced in 2017. Like other large language models, including BERT and GPT-3, LaMDA is trained on terabytes of text data to learn how words relate to one another and then predict what words are likely to come next.

NLP 98

NLP BERT Large Language Models Natural Language Processing 98

Probably Approximately a Scientific Blog

JANUARY 12, 2021

In the last 5 years, popular media has made it seem that AI is nearly if not already solved by deep learning, with reports on super-human performance on speech recognition, image captioning, and object recognition. Credit for much of the content goes to the co-instructors, but any errors are mine. She ordered foods. ").

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 52

Topbots

JULY 6, 2023

LaMDA is built on Transformer , a neural network architecture that Google Research invented and open-sourced in 2017. Like other large language models, including BERT and GPT-3, LaMDA is trained on terabytes of text data to learn how words relate to one another and then predict what words are likely to come next.

LLM 52

LLM Large Language Models Natural Language Processing OpenAI 52

Unite.AI

SEPTEMBER 20, 2023

Transformers in NLP In 2017, Cornell University published an influential paper that introduced transformers. These are deep learning models used in NLP. We'll also walk through the essential features of Hugging Face, including pipelines, datasets, models, and more, with hands-on Python examples.

LLM 342

LLM BERT NLP Python 342