Revolutionizing Language Models: The New TTT Architecture Surpasses Transformer

Revolutionizing Language Models: The New TTT Architecture Surpasses Transformer

Source: Machine Heart This article is approximately 3200 words long and is recommended for a 5-minute read. This article introduces a brand new large language model (LLM) architecture that is expected to replace the Transformer, which has been dominant in the AI field until now. From 125M to 1.3B large models, performance has improved. Incredible, … Read more

Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 2)

Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 2)

For More Content, Please Follow: Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 1) Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 2) 23-24 Basics of Recurrent Neural Networks As shown above, sequence problems such as named entity recognition account for a significant proportion of real-life applications, while traditional machine learning … Read more

Multivariate Multi-Step Prediction Model Based on LSTM

Multivariate Multi-Step Prediction Model Based on LSTM

♚ Author: Yishui Hancheng, CSDN Blog Expert, Research Directions: Machine Learning, Deep Learning, NLP, CV Blog: http://yishuihancheng.blog.csdn.net This article mainly practices multivariate sequence prediction based on LSTM (Long Short-Term Memory) neural networks, completing the prediction, analysis, and visualization of data at specified future time steps, and teaches you step by step how to build your … Read more

Transformer, CNN, GNN, RNN: Understanding Attention Mechanisms

Transformer, CNN, GNN, RNN: Understanding Attention Mechanisms

Follow the official account “ML_NLP“ Set as “Starred“, essential resources delivered first-hand! Looking back at the phrase from 2017, “Attention is all you need”, it truly was a prophetic statement. The Transformer model started with machine translation in natural language processing, gradually influencing the field (I was still using LSTM in my graduation thesis in … Read more

How to Handle Variable Length Sequences Padding in PyTorch RNN

How to Handle Variable Length Sequences Padding in PyTorch RNN

Follow us on WeChat “ML_NLP” Set as “Starred”, delivering valuable content to you first! Produced by Machine Learning Algorithms and Natural Language Processing Original Column Author on WeChat @ Yi Zhen School | PhD Student at Harbin Institute of Technology SCIR 1. Why RNN Needs to Handle Variable Length Inputs Assuming we have an example … Read more

Implementing Recurrent Neural Networks (RNNs) in Python for Time Series Prediction

Implementing Recurrent Neural Networks (RNNs) in Python for Time Series Prediction

Case Introduction This case will demonstrate how to use Recurrent Neural Networks (RNNs) for time series prediction. Specifically, we will use RNNs to predict the future values of a variable that depends on its own historical values. In this case, we will use a temperature dataset. We will provide the temperature values from the past … Read more

Deep Learning for NLP: ANNs, RNNs and LSTMs Explained!

Deep Learning for NLP: ANNs, RNNs and LSTMs Explained!

Author: Jaime Zornoza, Technical University of Madrid Translation: Chen Zhiyan Proofreading: Wang Weili This article is approximately 3700 words, and it is recommended to read in 10+ minutes. This article will help you understand deep learning neural networks in a way never seen before, and build a Chatbot using NLP! Have you ever fantasized about … Read more

Can A Concise Architecture Be Efficient And Accurate? Tsinghua & Huawei Propose A New Residual Recurrent Super-Resolution Model: RRN!

Can A Concise Architecture Be Efficient And Accurate? Tsinghua & Huawei Propose A New Residual Recurrent Super-Resolution Model: RRN!

Sharing a paper on video super-resolution titled Revisiting Temporal Modeling for Video Super-resolution, which is a BMVC 2020 paper. The results of this paper currently rank first on several datasets for video super-resolution, and the code has been open-sourced. Affiliations: Tsinghua University, New York University, Huawei Noah’s Ark Lab 1 Highlights This paper proposes a … Read more

SUPRA: Transforming Transformers into Efficient RNNs Without Extra Training

SUPRA: Transforming Transformers into Efficient RNNs Without Extra Training

This article is approximately 2600 words long and is recommended to be read in 9 minutes. The SUPRA method significantly improves model stability and performance by replacing softmax normalization with GroupNorm. Transformers have established themselves as the primary model architecture, particularly due to their outstanding performance across various tasks. However, the memory-intensive nature of Transformers … Read more

Understanding LSTM: A Comprehensive Guide

Understanding LSTM: A Comprehensive Guide

Friends familiar with deep learning know that LSTM is a type of RNN model that can conveniently handle time series data and is widely used in fields such as NLP. After watching Professor Li Hongyi’s deep learning videos from National Taiwan University, especially the first part introducing RNN and LSTM, I felt enlightened. This article … Read more