In my last post about Complete The Look, I tried to explain what Pinterest did with their fashion recommender system. If you did not see this post, you could follow this link to get the main ideas to accomplish this task.
Complete The Look is a promising approach in the attempt to overcome the limitation of traditional fashion recommender systems. The old-fashioned systems often use images of products on a plain white background, whereas what the customers want to see is the way these products complement each other in daily scenes such as in street photos, travel lookbooks, and selfies.
Pinterest’s Engineering team has recently posted their research in fashion recommendation, which capture my attention because of its practicality. In this post, they showed a new task in fashion recommendation called Complete The Look. Attempting to explore this task is great for any machine learning practitioner. There are some ambiguous terms that could make readers misunderstand. I will try to explain these terms in Part 1 and show how I reimplement the fashion recommender system in the next part.
When reaching the end of this tutorial, I hope you could understand:
Let’s try to summarize a paper about “How BTS Became The Undisputed Kings Of K-Pop”
Amazing summarize result
Machine Learning is a subset of Artificial Intelligence. It include supervisor learning, unsupervised learning, reinforcement learning and their combination. Since the ideas of artificial neural network, a subset of machine learning called Deep Learning, using neural network, was born.
Let’s talk about Deep Learning first, it use neural network to see how “important” the input effect desired output. A simple fully-connected neural network has 3 layers: input, output and hidden layer, all in numberic form. Input provides neural network the “vision”, the important features that effect the output. The output layer can be one value, or multidimension vector. …
The question answering system is commonly used in the field of natural language processing. It is used to answer questions in the form of natural language and has a wide range of application.
This blog post mainly deals with a Question Answering system designed for a specific field, which is usually use a model called Transformers and it makes use of several methods and mechanisms that I’ll introduce here. The papers I refer to in the post offer a more detailed and quantitative description.
At the end of 2018, researchers of Google AI Language have public a new model for…
Generative Adversarial Networks are a type of deep neural network architecture that uses unsupervised machine learning to generate data. They were introduced in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, which can be found at the following link: https://arxiv.org/pdf/1406.2661. GANs have many applications, including image generation and drug development.
This blog will introduce you to the core components of GANs. It will take you through how each component works and the important concepts and technology behind GANs. It will also give you a brief overview of the benefits and drawbacks of using GANs and an…