I’ve never worked in the field of computer vision and has no idea how the magic could work when an autonomous car is configured to tell apart a stop sign from a pedestrian in a red hat. To motivate myself to look into the maths behind object recognition and detection algorithms, I’m writing a few posts on this topic “Object Detection for Dummies”. This post, part 1, starts with super rudimentary concepts in image processing and a few methods for image segmentation....

Human vocabulary comes in free text. In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values. One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0). However, one-hot encoding is impractical computationally when dealing with the entire vocabulary, as the representation demands hundreds of thousands of dimensions....

Professor Naftali Tishby passed away in 2021. Hope the post can introduce his cool idea of information bottleneck to more people. Recently I watched the talk “Information Theory in Deep Learning” by Prof Naftali Tishby and found it very interesting. He presented how to apply the information theory to study the growth and transformation of deep neural networks during training. Using the Information Bottleneck (IB) method, he proposed a new learning bound for deep neural networks (DNN), as the traditional learning theory fails due to the exponentially large number of parameters....

[Updated on 2018-09-30: thanks to Yoonju, we have this post translated in Korean!] [Updated on 2019-04-18: this post is also available on arXiv.] Generative adversarial network (GAN) has shown great results in many generative tasks to replicate the real-world rich content such as images, human language, and music. It is inspired by game theory: two models, a generator and a critic, are competing with each other while making each other stronger at the same time....

The machine learning models have started penetrating into critical areas like health care, justice systems, and financial industry. Thus to figure out how the models make the decisions and make sure the decisioning process is aligned with the ethnic requirements or legal regulations becomes a necessity. Meanwhile, the rapid growth of deep learning models pushes the requirement of interpreting complicated models further. People are eager to apply the power of AI fully on key aspects of everyday life....

In the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input. Dataset During the search, I found this library for querying Yahoo!...

This is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. The full working code is available in github.com/lilianweng/stock-rnn. If you don’t know what is recurrent neural network or LSTM cell, feel free to check my previous post. One thing I would like to emphasize that because my motivation for writing this post is more on demonstrating how to build and train an RNN model in Tensorflow and less on solve the stock prediction problem, I didn’t try hard on improving the prediction outcomes....

(The post was originated from my talk for WiMLDS x Fintech meetup hosted by Affirm.) I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol in 2016. Lee has the highest rank of nine dan and many world championships. No doubt, he is one of the best Go players in the world, but he lost by 1-4 in this series versus AlphaGo....