The intuition behind Shannon’s Entropy

Now, back to our formula 3.49: The definition of Entropy for a probability distribution (from The Deep Learning Book) I(x) is the information content of X. I(x) itself is a random variable. In our example, the possible outcomes of the War. Thus, H(x) is the expected value of every possible information. Using the definition of expected … Read more

Image Processing Class (EGBE443) #2 -Histogram

Computing the histogram In this section, the histogram was calculated by implementation of python programming code (Python 3.6). For python 3.6, There are a lot of common modules using in image processing such as Pillow, Numpy, OpenCV, etc. but in this program Pillow and Numpy module was used. To import the image from your computer, … Read more

Lazy Neural Networks

Before I get into solutions I think it is important to discuss some overarching themes of deep learning. Training Objectives Remember that when we create a neural network, what we are effectively doing is designing an experiment. We have data, a model architecture and a training objective. The data you provide is the models universe … Read more

How to get fbprophet working on AWS Lambda

Solving package size issues of fbprophet serverless deployment Adi Goldstein / Unsplash I assume you’re reading this post because you’re looking for ways to use the awesome fbprophet (Facebook open source forecasting) library on AWS Lambda and you’re already familiar with the various issues around getting it done. I will be using a python 3.6 … Read more

Multi-Layer perceptron using Tensorflow

Sep 11, 2018 In this blog, we are going to build a neural network(multilayer perceptron) using TensorFlow and successfully train it to recognize digits in the image. Tensorflow is a very popular deep learning framework released by, and this notebook will guide for build a neural network with this library. If you want to understand … Read more

Diving into K-Means…

Sep 9, 2018 We have completed our first basic supervised learning model i.e. Linear Regression model in the last post here. Thus in this post we get started with the most basic unsupervised learning algorithm- K-means Clustering. Let’s get started without further ado! Background: K-means clustering as the name itself suggests, is a clustering algorithm, … Read more

3 approaches for backtesting historical data

Reading and processing data for statistical and quantitative analysis in trading Sep 8, 2018 Anyone interested in the statistical analysis of financial markets has the need to process historical data. Historical data is needed in order to backtest or train: Quantitative trading. Statistical trading. Price action replay/walkthrough. Each need comes from different goals. 3 examples on … Read more

Microsoft Big Data Overview

https://academy.microsoft.com/en-us/professional-program/tracks/big-data/ Block 1 – Data Fundamentals Learn data science basics. Explore topics like data queries, data analysis, data visualization and how statistics informs data science practices. Please choose from Course 2a or Course 2b to complete the unit. Course 1: Microsoft Professional Program: Introduction to Big Data Course 2a: Analyzing and Visualizing Data with Power … Read more

Box Cox Transformation

When we do time series analysis, we are usually interested either in uncovering causal relationships (Does \(X_t\) influence \(Y_{t+1}\)?) or in getting the most accurate forecast possible. Especially in the second case it can be beneficial to transform our historical data to make it easier to extract a signal. A very common transformation is to … Read more