Data Science Austria

Review: Suggestive Annotation — Deep Active Learning Framework (Biomedical Image Segmentation)

Reducing Annotation Effort & Cost of Biomedical Experts such as Radiographer Glands Segmentation in Colon Histology Images (Left) & Lymph Nodes Segmentation in Ultrasound Images (Right) In this story, Suggestive Annotation (SA) is reviewed. For example, colon cancer and cancer in lymph nodes (lymphoma), are two common types of cancers causing … Read moreReview: Suggestive Annotation — Deep Active Learning Framework (Biomedical Image Segmentation)

Autoencoders: Deep Learning with TensorFlow’s Eager API | Data Stuff

We are so deep. Source: Pixabay. Deep Learning has revolutionized the Machine Learning scene in the last years. Can we apply it to image compression? How well can a Deep Learning algorithm reconstruct pictures of kittens? What’s an autoencoder? Today we’ll find the answers to all of those questions. Image Compression: … Read moreAutoencoders: Deep Learning with TensorFlow’s Eager API | Data Stuff

Audio Classification with Pre-trained VGG-19 (Keras)

Taken from https://www.semanticscholar.org/paper/Raw-Waveform-based-Audio-Classification-Using-CNN-Lee-Kim/09be9adf2a925da20db12918283c54a9044272af/figure/0 In this post, I’ll target the problem of audio classification. I’ll train an SVM classifier on the features extracted by a pre-trained VGG-19, from the waveforms of audios. The main idea behind this post is to show the power of pre-trained models, and the ease with which … Read moreAudio Classification with Pre-trained VGG-19 (Keras)

How I made 37% annual return for 3 yrs using data science, machine learning and TALF loans

In this post, I will continue with my series of practical applications in the use of data science. This time, a practical application involving data science and financial quantitative analysis to analyze investment opportunities in fixed income instruments. This exercise refers to the opportunity generated by the Term Asset Loan … Read moreHow I made 37% annual return for 3 yrs using data science, machine learning and TALF loans

Linear Regression with PyTorch

Linear Regression is an approach that tries to find a linear relationship between a dependent variable and an independent variable by minimizing the distance as shown below. Taken from https://www.youtube.com/watch?v=zPG4NjIkCjc In this post, I’ll show how to implement a simple linear regression model using PyTorch. Let’s consider a very basic … Read moreLinear Regression with PyTorch

Detecting a simple neural network architecture using NLP for email classification

Hyper parameter optimization in email classification. About a decade ago, spam brought email to near-ruin. By 2015, Google says that its spam rate is down to 0.1 percent, and its false positive rate has dipped to 0.05 percent. The significant drop in large part is due to the introduction of … Read moreDetecting a simple neural network architecture using NLP for email classification

Linear programming and discrete optimization with Python using PuLP

Linear and integer programming are key techniques for discrete optimization problems and they pop up pretty much everywhere in modern business and technology sectors. We will discuss how to tackle such problems using Python library PuLP and get a fast and robust solution. Introduction Discrete optimization is a branch of … Read moreLinear programming and discrete optimization with Python using PuLP

If We Care For Robots, Who Will Care For Us?

After my exit interview, the human researcher informed me that this particular robot was remotely controlled and voiced —what’s known as the “Wizard of Oz” research technique — so only appeared to have the programming to respond in-time to me. This came as a total surprise to me; I just figured universities … Read moreIf We Care For Robots, Who Will Care For Us?