The following problems are taken from the projects / assignments in the edX course Python for Data Science and the coursera course Applied Machine Learning in Python (UMich).

● The IMDB Movie Dataset (MovieLens 20M) is used for the analysis.

● The dataset is downloaded from …

Added by Sandipan Dey on December 16, 2017 at 1:30pm — 5 Comments

he following problems are taken from a few assignments from the coursera courses * Introduction to Deep Learning (by Higher School of Economics) *and

Added by Sandipan Dey on November 25, 2017 at 2:00pm — 4 Comments

The following problems appeared as a programming assignment in the Computation Photography course (CS445) at UIUC. The description of the problem is taken from the assignment itself. In this assignment, a python implementation of the problems will be described instead of matlab, as expected in the course.

- The goal of this assignment is to implement the
for**image quilting algorithm**

texture synthesis and transfer,…

Added by Sandipan Dey on October 24, 2017 at 1:30pm — No Comments

The following problem appeared as an assignment in the ** Algorithm Course** (

The Seam Carving Problem

is a*Seam-carving*technique where the image…*content-aware image resizing*

Added by Sandipan Dey on October 14, 2017 at 2:30pm — No Comments

*Long title: Measuring Semantic Relatedness using the Distance and the Shortest Common Ancestor and Outcast Detection with Wordnet Digraph in Python*

The following problem appeared as an assignment in the ** Algorithm Course** (

Added by Sandipan Dey on October 13, 2017 at 12:30pm — No Comments

The goal of *Poisson image editing* is to perform *seamless blending* of an *object* or a *texture*from a* source image* (captured by a *mask image*) to a *target image*. We want to create a photomontage by pasting an image region onto a new background using Poisson image editing. This idea is from the *P´erez et al*’s *SIGGRAPH 2003* paper …

Added by Sandipan Dey on October 3, 2017 at 10:00pm — No Comments

In this article, some more social networking concepts will be illustrated with a few problems. The problems appeared in the programming assignments in the

* coursera *course

The following theory is going to be used to solve the assignment problems.

…

ContinueAdded by Sandipan Dey on September 21, 2017 at 11:30pm — No Comments

The following problems appeared in the programming assignments in the * coursera *course

The following theory is going to be used to solve the assignment problems.

**1. Creating and Manipulating…**

Added by Sandipan Dey on September 20, 2017 at 11:30pm — 6 Comments

The following problem appeared as an assignment in the *coursera course Algorithm-I* by *Prof.Robert Sedgewick ** *from the Princeton University few years back (and also in the course *cos226* offered at *Princeton*). The problem definition and the description is taken from the course website and lectures. The original assignment was to be done in java, where in this article both the *java* and a corresponding *python* implementation will…

Added by Sandipan Dey on September 11, 2017 at 4:30am — No Comments

In this article, an *R-hadoop* (with *rmr2*) implementation of ** Distributed KMeans Clustering **will be described with a

- First the dataset shown below is
into 4 data subsets and they are copied from*horizontally partitioned**local*to, as shown in the following animation. The dataset chosen is small enough and it’s just for the*HDFS**POC*purpose,…

Added by Sandipan Dey on September 1, 2017 at 11:30am — No Comments

In this article, we shall see how the ** Bayesian Kalman Filter** can be used to predict positions of some moving particles / objects in 2D.

This article is inspired by a programming assignment from the

The following…

ContinueAdded by Sandipan Dey on August 31, 2017 at 12:30pm — No Comments

Given a set of labeled images of ** cats** and

- The original dataset contains a huge number of images, only a few sample images are chosen (
*1100*labeled images for cat/dog as training…

Added by Sandipan Dey on August 14, 2017 at 1:00pm — 2 Comments

The following problems appeared in the assignments in the **Udacity course Deep Learning (by Google)**. The descriptions of the problems are taken from the assignments (continued from the last post).

Here is how some sample images from the dataset look like:

Let’s try to get the best performance using a multi-layer model!…

ContinueAdded by Sandipan Dey on August 3, 2017 at 10:30pm — No Comments

- In this article, a mathematical model for the
(shown below) will be described (reference: the video lectures of Prof. Jeffrey R Chesnov from Coursera Course on*growth of a sunflower***Fibonacci numbers**). - New florets are created close to center.

- Florets move radially out with constant speed as the sunflower grows.

- Each new…

Added by Sandipan Dey on August 1, 2017 at 1:30am — No Comments

- In this assignment, some
*exploratory analysis*is done on the**criminal incident data**from**Seattle**and**San Francisco**to visualize patterns and contrast and compare patterns across the two cities. **Data**used: The real crime dataset from**Summer (June-Aug) 2014**for both of two US cities*Seattle*and*San Francisco*has been used for the analysis. The datasets used for…

Added by Sandipan Dey on July 31, 2017 at 4:00am — No Comments

The following problems appeared as assignments in the **coursera course** **Data-Driven Astronomy**.

…

Added by Sandipan Dey on July 29, 2017 at 12:00pm — No Comments

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

**Summary**

In this report, the spread of the **pandemic influenza A** (**H1N1**) that had an outbreak in Kolkata, West Bengal, India, 2010 is going to be simulated. The basic **epidemic SIR model** will be used, it…

Added by Sandipan Dey on July 21, 2017 at 8:30am — No Comments

The following problems appeared in the first few assignments in the **Udacity course Deep Learning (by Google)**. The descriptions of the problems are taken from the assignments.…

Added by Sandipan Dey on June 17, 2017 at 1:30pm — No Comments

This shinyapp is a **live shiny/R web application** (hosted on shinyapps.io) that implements **simple sentiment analysis** POC with **R**, to have an insight about the people's sentiment about the *smartphones* from different ** brands** released in

Added by Sandipan Dey on June 9, 2017 at 12:30pm — 3 Comments

In this article, the *clustering output results* using * Spectral clustering (with normalized Laplacian)* are going to be compared with taht obtained using

The following couple of slides taken from the *Coursera Course: Mining Massive Datasets by Stanford University*

describe the basic concepts behind…

Added by Sandipan Dey on June 8, 2017 at 11:00am — 2 Comments

- ‹ Previous
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- Solving a few AI problems with Python: Part 1
- Fourier Series and Differential Equations with some applications in R and Python
- Fourier Series and Differential Equations with some applications in R and Python (Part 2)
- Fourier Series and Differential Equations with some applications in R (Part 1)
- Solving Some Image Processing Problems with Python libraries - Part 3
- Solving Some Image Processing Problems with Python libraries - Part 2
- Solving Some Image Processing Problems with Python libraries - Part 1

- SIR Epidemic model for influenza A (H1N1): Modeling the outbreak of the pandemic in Kolkata, West Bengal, India, 2010
- Data Science with Python: Exploratory Analysis with Movie-Ratings and Fraud Detection with Credit-Card Transactions
- Autonomous Driving – Car detection with YOLO Model with Keras in Python
- Deep Learning with TensorFlow in Python
- Some Deep Learning with Python, TensorFlow and Keras
- Some Applications of Markov Chain in Python
- Some Social Network Analysis with Python

- Data (2)
- Learning (2)
- Python (2)
- Analysis (1)
- Astronomy (1)
- Chain (1)
- Classification (1)
- CrossMatching (1)
- Deep (1)
- Differential (1)
- Equation (1)
- Exploratory (1)
- Fourier (1)
- Machine (1)
- Markov (1)
- Math (1)
- Mathematical (1)
- Modeling (1)
- R (1)
- Science (1)
- Semisupervised (1)
- Tensorflow (1)
- Transform (1)
- Visualization (1)

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