Data Science and AI Blog.
Our Data Scientist / AI Engineer is writing about
Data Science / Mathematics / Statistics / Engineering.
In this blog post, I am going to introduce one of the most intuitive algorithms in the field of Supervised Learning[1], the k-Nearest Neighbors algorithm (kNN). The original k-Nearest Neighbors algorithm The kNN algorithm is very intuitive. Indeed, with the assumption that items close together in the dataset are typically similar, kNN infers the output …
Make your Machine Learning team working easier, focus more on business and quick deployment with AWS managed service SageMaker. Today, Machine Learning(ML) is resolving complex problems which make more business values for customer and many companies also apply ML to resolve robust business problems. ML have more benefit, but also more challenges to building the …
Machine Learning development with AWS Sage Maker Read More »
I. A Brief Overview Consider an example of a courtroom trial: A car company C is accused of not manufacturing environment-friendly vehicles. The average CO2 emission per car from different manufacturers based on a survey from the previous year is 120.4 grams per kilometer. But for a random batch of 100 cars produced at C’s …
In recent years, there have been a few prominent examples of accidental bias in machine-learning applications, such as smartphones’ beauty filters (that essentially ended up whitening skin) [1] or Microsoft’s from-innocent-teen-to-racist-in-24-hours chatbot [2,3]. Examples such as these fell victim to inherently biased data being fed into algorithms too complex to allow for much transparency. Hidden bias continues …
Bias in Data Science – the Good, the Bad and the Avoidable !? Read More »
Motivation What makes computers useful for us is primarily the ability to solve problems. The procedure in which computers solve a problem is an algorithm. In the recent context of an increasing number of algorithms available for solving data-related problems, there is increasing demand for a higher level of understanding of algorithm’s performance for data …
Can you expand on ? I guess you would find that is quite easy to do. You can easily find that . How about the expansion of . It is no longer easy. It is no longer easy, isn’t it. However, if we use Binomial Theorem, this expansion becomes an easy problem. Binomial Theorem is a very intriguing topic …
On a nice day 2 years ago, when I was in the financial field. My boss sent our team an email. In this email, he would like to us propose some machine learning techniques to predict stock price. So, after accepting the assignment from my manager, our team begin to research and apply some approaches …
In this post, I will explain how to calculate a Bayesian estimator. The taken example is very simple: estimate the parameter θ of a Bernoulli distribution. A random variable X which has the Bernoulli distribution is defined as with In this case, we can write . In reality, the simplest way …
Background In part 1 of my project, I built a unigram language model: it estimates the probability of each word in a text simply based on the fraction of times the word appears in that text. The text used to train the unigram model is the book “A Game of Thrones” by George R. R. Martin (called train). …
Background Language modeling — that is, predicting the probability of a word in a sentence — is a fundamental task in natural language processing. It is used in many NLP applications such as autocomplete, spelling correction, or text generation. Currently, language models based on neural networks, especially transformers, are the state of the art: they predict very accurately a …
