Browsing Category

Deep Learning

15 posts
Photo by Anthony Tran on Unsplash
Read More
  • 52 views
  • 10 minute read

Attention Models

Attention mechanisms is a method in deep learning that lets a model focus on the most relevant parts of its input when producing each piece of its output. Unlike traditional sequence models that often struggle with longer inputs, attention allows models to dynamically focus on different parts of the input sequence when generating each part of the output sequence.
Read More
0
0
0
0
Photo by Léonard Cotte on Unsplash
Read More
  • 50 views
  • 5 minute read

Sequence to Sequence Model (Seq2Seq)

Sequence to Sequence (Seq2Seq) model is a neural network architecture that maps one sequence to another. It has revolutionized the field of Natural Language Processing (NLP), significantly enhancing the performance of tasks such as translation, text summarization, and chatbots. This article will dive deeply into the principles behind the Seq2Seq model.
Read More
0
0
0
0
Photo by Daniele Buso on Unsplash
Read More
  • 70 views
  • 10 minute read

Bi-directional Recurrent Neural Networks (BRNNs)

Bi-directional recurrent neural betworks (BRNNs) are an extension of standard RNNs specifically designed to process sequential data in both forward and backward directions. Compared to traditional RNNs, BRNN architectures maintain more comprehensive context information, enabling them to capture useful dependencies across entire sequences for improved predictions in various natural language processing and speech recognition tasks.
Read More
0
0
0
0
Photo by Kelsey Curtis on Unsplash
Read More
  • 69 views
  • 5 minute read

GloVe Word Embeddings

GloVe is a word embedding model that constructs word vectors based on global co-occurrence statistics. Unlike Word2Vec, which relies on local context windows, GloVe captures the overall statistical relationships between words through matrix factorization. This approach enables GloVe to generate high-quality word representations that effectively encode semantic and syntactic relationships. This article will introduce the principles and training methods of GloVe.
Read More
0
0
0
0
Photo by Barcs Tamás on Unsplash
Read More
  • 108 views
  • 10 minute read

Word2Vec Word Embedding Model

Word2Vec is a model for learning word embeddings, which converts words and their semantics into vectors through neural networks. Word2Vec provides two training methods: CBOW and Skip-gram, and improves efficiency through Negative Sampling and Subsampling technologies. This article will introduce the basic principles and training methods of Word2Vec.
Read More
0
0
0
0