在2017年《Transformer》论文发表之前,大多数NLP任务都是通过使用带有注意力机制的RNN来获得的,所以Attention在Transformer之前就存在了。通过单独引入多头注意力机制,并放弃RNN部分,transformer架构通过允许多个独立的注意力机制,来从不同角度抽取每个token的上下文语义。 在这篇文章中,我们将介绍这个架构的一个细节,即Query...
The first is semantic understanding, that is to say the problem of learning knowledge or common sense. This problem is about how NLP technology can get “deeper”. Although humans don’t have any problem understanding common sense, it’s very difficult to teach this to machines. For example,...
Before the application of deep learning techniques in NLP, the mathematical tools used were completely different to the ones adopted for speech, image, and video processing, creating a huge barrier to the flow of information between these different modes. But using deep learni...
The performance is similar to that of Java or C++. For our use case, Go is typically 30 times faster than Python. Here’s a small benchmark game comparing Go vs Java. 原因1 - 表现:Go很快!Go确实快。其表现类似于Java或C++。在我们的使用情况,Go通常比Python快30倍。以下是一个比较Go和Java...
GPT-4 is the latest version of Generative Pre-trained Transformers, a type of deep learning model used for natural language processing and text generation. It marks a significant milestone in the field of artificial intelligence, particularly in natural language processing. What are the capabilities ...
From 2012 to 2018, Convolutional Neural Networks (CNNs) gained popularity with the use of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for audio and voice neural networks. This changed rapidly with the introduction of the “Attention Is All You ...
BERT models are able to understand the nuances of expressions at a much finer level. For example, when processing the sequence “Bob needs some medicine from the pharmacy. His stomach is upset, so can you grab him some antacids?” BERT is better able to understand that “Bob,”“his”,...
Note: The purpose of this section (3. The Data) is to show the data preprocessing and to give rationale for using different sources of data, hence I will only use a subset of the full data (that is used for training). def parser(x): return datetime.datetime.strptime(x,'%Y-%m-%d'...
Or to put it more bluntly: the unsupervised domain adaptation problem is still far from being solved by fine-tuning pre-trained models using only source domain data. The orange bar represents training an RNN from scratch using source domain data (laptop reviews) and performing inference using ...
Going into the details of BERT and the NLP part is not in the scope of this notebook, but you have interest, do let me know - I will create a new repo only for BERT as it definitely is quite promissing when it comes to language processing tasks. 3.4. Fourier transforms for trend ...