Click the blue text aboveComputer Vision Alliance to get more valuable content Set as favorite in the upper right corner ··· and we won’t miss each other This is for academic sharing only and does not represent the stance of this public account. Contact for removal in case of infringement Reprinted from: Author: Lucas Address: … Read more
[GiantPandaCV Guide] In recent years, Attention-based methods have gained popularity in both academia and industry due to their interpretability and effectiveness. However, the network structures proposed in papers are often embedded within code frameworks for classification, detection, segmentation, etc., leading to redundancy in code. For beginners like me, it can be challenging to find the … Read more
▼Recently, there have been a lot of live broadcasts,make an appointment to ensure you gain something. The RAG (Retrieval-Augmented Generation) technology is detailed in the article “Understanding RAG: A Comprehensive Guide to Retrieval-Augmented Generation,” with a typical RAG case shown in the image below, which includes three steps: Indexing: Split the document library into shorter … Read more
Selected from arXiv Author:Mohammad Shoeybi et al. Translated by Machine Heart Contributors:Mo Wang Previously, Machine Heart introduced a study by NVIDIA that broke three records in the NLP field: reducing BERT’s training time to 53 minutes; reducing BERT’s inference time to 2.2 milliseconds; and increasing the parameter count of GPT-2 to 8 billion (previously, GPT-2 … Read more
Selected from GitHub Author: Junseong Kim Translated by Machine Heart Contributors: Lu Xue, Zhang Qian Recently, Google AI published an NLP paper introducing a new language representation model, BERT, which is considered the strongest pre-trained NLP model, setting new state-of-the-art performance records on 11 NLP tasks. Today, Machine Heart discovered a PyTorch implementation of BERT … Read more
01 Introduction The Tongyi Qianwen team has launched the first MoE model in the Qwen series, Qwen1.5-MoE-A2.7B. It has only 2.7 billion activated parameters, but its performance can rival that of current state-of-the-art models with 7 billion parameters, such as Mistral 7B and Qwen1.5-7B. Compared to Qwen1.5-7B, which contains 6.5 billion Non-Embedding parameters, Qwen1.5-MoE-A2.7B has … Read more
Warning: This may be the easiest to understand, easiest to run example for efficient fine-tuning of various open-source LLM models, supporting both multi-turn and single-turn dialogue datasets. We constructed a toy dataset of three rounds of dialogue that modifies the self-awareness of the large model, using the QLoRA algorithm, which can complete fine-tuning in just … Read more
1 Introduction This article mainly introduces a very classic algorithm in word embedding, Word2Vec. Initially, Word2Vec was primarily used in text-related problems, but now friends participating in competitions should have noticed that almost half of the traditional data competitions involve Word2Vec. Therefore, we must take a good look at what Word2Vec is actually learning, so … Read more
Click the “AI Insight” above and select “Star” to follow the public account. Heavyweight content delivered first-hand. From: Open Source Frontline (ID: OpenSourceTop) Comprehensive from: https://github.com/yuanxiaosc/DeepNude-an-Image-to-Image-technology, programmers, etc. Some time ago, a programmer developed an application called DeepNude. “Is Technology Innocent?” The AI stripping app was taken offline just hours after its launch. The app … Read more
Author: Mirko Translated by: Sour Bun Wishing you a peaceful Dragon Boat Festival Generative Adversarial Network Applications in Quantitative Investing Series (Part 1) Get the complete code at the end of the article 1 Introduction Overfitting is one of the challenges we face when applying machine learning techniques to time series. This issue arises because … Read more