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. … Read moreThe intuition behind Shannon’s Entropy
Sep 29, 2018 Nearly all sectors use time series data to forecast future time points. Forecasting future can assist analysts and management in making better calculated decisions to maximise returns and minimise risks. I will be demonstrating how we can forecast exchange rates in this article. If you are new … Read moreForecasting Exchange Rates Using ARIMA In Python
Fast RCNN So the next idea from the same authors: Why not create convolution map of input image and then just select the regions from that convolutional map? Do we really need to run so many convnets? What we can do is run just a single convnet and then apply … Read moreObject Detection using Deep Learning Approaches: An End to End Theoretical Perspective
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 … Read moreImage Processing Class (EGBE443) #2 -Histogram
Hi all, there is a very quick guide how to configure a system monitoring for one or more servers using a modern stack of technologies like Grafana, Docker and Telegraf with Influxdb. The main goal for this article is to show how to start geting system metrics from your servers … Read moreGet system metrics for 5 min with Docker, Telegraf, Influxdb and Grafana
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 … Read moreLazy Neural Networks
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. … Read moreMulti-Layer perceptron using Tensorflow
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 … Read moreDiving into K-Means…
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 … Read more3 approaches for backtesting historical data