Click on the above “Beginner’s Visual Learning” to select “Add to Favorites” or “Pin” Essential content delivered immediately Source: Python Data Science This article is about 7200 words long and is recommended to read in 14 minutes. In this article, we will explore the Transformer model and understand how it works. 1. Introduction Google’s BERT … Read more
Selected from Medium Translated by Machine Heart Contributors: Lu Xue, Li Zenan Not long ago, Andrew Ng’s fourth course on Convolutional Neural Networks was released on Coursera. This article is a reflection written by Ryan Shrott, Chief Analyst at the National Bank of Canada, after completing the course, which helps everyone intuitively understand and learn … Read more
Machine Heart reports Author: Danjiang Coursera has just launched a special course on GAN, which you might consider taking during this National Day holiday. Generative Adversarial Network (GAN) is one of the most powerful machine learning models today, capable of generating realistic images, videos, and audio outputs. Applications based on GAN are extensive, such as … Read more
Selected from Medium Author: Arvind N Translated by Machine Heart Contributors: Lu Xue, Li Zenan On August 8, Andrew Ng officially launched Deeplearning.ai—a series of deep learning courses based on Coursera, aiming to spread foundational knowledge of artificial intelligence to more people. A week later, many have completed the first three courses that are currently … Read more
Edited by Machine Heart Contributors: Siyuan, Wang Shuting This month, Google’s BERT has received a lot of attention, as the research has refreshed the state-of-the-art performance records in 11 NLP tasks with its pre-trained model. The authors of the paper stated that they would release the code and pre-trained model by the end of this … Read more
BERT has been very popular recently, so let’s gather some related resources, including papers, code, and article interpretations. 1. Official Google resources: 1) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Everything started with this paper released by Google in October, which instantly ignited the entire AI community, including social media: https://arxiv.org/abs/1810.04805 2) GitHub: … Read more
In Chapter 2, we saw what is needed to fine-tune and evaluate a Transformer. Now let’s take a look at how they work under the hood. In this chapter, we will explore the main components of the Transformer model and how to implement them using PyTorch. We will also provide guidance on how to do … Read more
Fingerprint unlocking, facial recognition, speech-to-text, robots diagnosing diseases, Alphago······ We have profoundly felt that artificial intelligence is changing our work methods and perceptions. According to the report on enterprise AI readiness by SAS, most companies believe that artificial intelligence is still in its early stages, “Currently, many application scenarios we are deploying contain AI components”. … Read more
Click on the “CVer” above to select “Star” or “Top” Heavyweight content delivered promptly Author: Yu Zhenbo https://zhuanlan.zhihu.com/p/77952356 This article is authorized by the author and cannot be reproduced without permission. Recently, I just started using PyTorch and have encountered quite a few pitfalls. I record them here, as I feel they are common issues … Read more
In the field of machine learning, building flexible and easily adjustable models is an essential skill for advanced machine learning engineers when faced with various complex and changing business problems. However, many engineers still have a misconception that mastering just one deep learning framework is enough to succeed. In fact, there is no single framework … Read more