Viso.ai

APRIL 25, 2024

Today’s boom in CV started with the implementation of deep learning models and convolutional neural networks (CNN). We’ve split the lightweight models into four different categories: face recognition, healthcare, traffic, and general-purpose machine learning models. Learn more by booking a demo.

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Computer Vision Deep Learning Convolutional Neural Networks Python 52

AWS Machine Learning Blog

MAY 14, 2024

These models are pre-trained on massive datasets and, to sometimes fine-tuned with smaller sets of more task specific data. An important aspect of developing effective generative AI application is Reinforcement Learning from Human Feedback (RLHF). Therefore, you can use either or both based on your needs.

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Generative AI LLM Chatbots Machine Learning 88

NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). How much monolingual back-translated data is necessary to see a significant impact in MT quality?

BERT 52

BERT Natural Language Processing Computational Linguistics NLP 52

AssemblyAI

MAY 3, 2023

In the past months, an exquisitely human-centric approach called Reinforcement Learning from Human Feedback (RLHF) has rapidly emerged as a tour de force in the realm of AI alignment. Thanks to the widespread adoption of ChatGPT, millions of people are now using Conversational AI tools in their daily lives.

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Large Language Models Neural Network LLM Chatbots 363

The MLOps Blog

JUNE 27, 2023

As you delve into the landscape of MLOps in 2023, you will find a plethora of tools and platforms that have gained traction and are shaping the way models are developed, deployed, and monitored. Open-source tools have gained significant traction due to their flexibility, community support, and adaptability to various workflows.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

PyImageSearch

OCTOBER 2, 2023

Home Table of Contents A Deep Dive into Variational Autoencoder with PyTorch Introduction Comparison with Convolutional Autoencoder Architecture Latent Space Loss Function Why Does VAE Stand Out? The highlight will be the VAE training on the Fashion-MNIST data, followed by a detailed post-training analysis.

Computer Vision 120

Computer Vision Deep Learning Neural Network Auto-complete 120

PyImageSearch

MARCH 4, 2024

Just like a parrot learns human language by listening and mimicking, an LLM has been trained on a mind-boggling amount of text data — code, books, articles, you name it! LLMs are awesome tools, not sentient beings plotting world domination. Learning to translate English into Kalamang (a rare language with less than 200 speakers!)

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Large Language Models Computer Vision AI AI 67

PyImageSearch

FEBRUARY 19, 2024

vs. Gemini Pro for Image Classification Summary and Key Takeaways Citation Information Image Classification with Gemini Pro In this tutorial, you’ll learn how to use the Gemini Pro generative model with the Google AI Python SDK (software development kit) to generate code for image classification in PyTorch. ChatGPT-3.5 ChatGPT-3.5

Computer Vision 60

Computer Vision ChatGPT Deep Learning Python 60

Flipboard

SEPTEMBER 4, 2023

Home Table of Contents Getting Started with Docker for Machine Learning Overview: Why the Need? This lesson is the 1st of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning (this tutorial) Lesson 2 Lesson 3 Overview: Why the Need? What Are Containers?

Machine Learning 109

Machine Learning Computer Vision Deep Learning Python 109

Explosion

NOVEMBER 21, 2019

al, 2015) is a twist on the word2vec family of algorithms that lets you learn more interesting word vectors. Before training the model, the text is preprocessed with linguistic annotations , to let you learn vectors for more precise concepts. accuracy. For more examples and the full API documentation, see the GitHub repo.

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52

PyImageSearch

FEBRUARY 5, 2024

Introduction to Model Evaluation in Face Recognition In this tutorial, we will take this further and learn how to evaluate our trained model using Keras and TensorFlow. This will allow us to get a sense of its performance on novel unseen data and quantify its generalization capabilities for real-world applications.

Computer Vision 60

Computer Vision Deep Learning Python Convolutional Neural Networks 60

PyImageSearch

FEBRUARY 12, 2024

Introduction to Gemini Pro Vision Image Processing with Gemini Pro (this tutorial) Lesson 3 Lesson 4 Lesson 5 Lesson 6 To learn how to use Gemini Pro for generating various image processing techniques and to understand its comparative performance against ChatGPT-3.5, For comparison, OpenAI’s GPT-3.5 just keep reading.

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Computer Vision Python Deep Learning ChatGPT 64

The MLOps Blog

MARCH 12, 2024

TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. LLMOps encompasses best practices and a diverse tooling landscape. You will learn: The unique challenges LLMOps addresses.

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

Chatbots Life

MAY 12, 2023

a deep dive Unless you have been living under a rock for the last few months, you have probably heard about a new model from OpenAI called ChatGTP. Unfortunately, the model’s release wasn’t accompanied by a research paper, and its only official description can be found on the OpenAI blog. But what is a language model?

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ChatGPT Software Engineer NLP Prompt Engineer 105

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Where does it [DAGWorks] fit?

ML 52

ML Data Scientist Software Engineer Machine Learning 52

Explosion

JUNE 18, 2021

In this blog post, I’m going to discuss some of the biggest challenges for applied NLP and translating business problems into machine learning solutions. This blog post is based on talks I gave at the “Teaching NLP” workshop at NAACL 2021 and the L3-AI online conference. You can also watch the video here or view the slides.

NLP 52

NLP Machine Learning Natural Language Processing DevOps 52

Explosion

JANUARY 12, 2018

While we wouldn’t say bootstrapping is for everyone, it’s been a joy to build the company that we want to build, the way we want to build it: Worked with some amazing companies and shipped custom, cutting-edge machine learning solutions for a range of exciting problems. Implemented 9 annotation interfaces and over 20 built-in recipes.

NLP 52

NLP Neural Network Machine Learning Python 52

The MLOps Blog

JANUARY 19, 2024

Large language models Large Language Models (LLMs) are huge deep-learning models pre-trained on vast data. Related post Tokenization in NLP: Types, Challenges, Examples, Tools Read more For training, we’ll create a so-called prompt that contains not only the question and the context but also the answer.

LLM 52

LLM Auto-complete Large Language Models Natural Language Processing 52

Google Research AI blog

JANUARY 18, 2023

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the Google Research community Today we kick off a series of blog posts about exciting new developments from Google Research. I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models.

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Computer Vision Auto-classification Large Language Models Neural Network 132

AWS Machine Learning Blog

NOVEMBER 29, 2023

In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with unprecedented proficiency. Furthermore, evaluating LLMs can also help mitigating security risks, particularly in the context of prompt data tampering.

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The MLOps Blog

MARCH 21, 2023

Moving across the typical machine learning lifecycle can be a nightmare. From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. How to understand your users (data scientists, ML engineers, etc.).

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Flipboard

DECEMBER 6, 2023

In a world full of data, summarizing long texts into brief summaries saves time and helps make informed decisions. Tools like LangChain , combined with a large language model (LLM) powered by Amazon Bedrock or Amazon SageMaker JumpStart , simplify the implementation process.

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BERT Large Language Models Artificial Intelligence Artificial Intelligence 128