Dr. Sasanko S Gantayat's Projects
A collection of python projects
comparing english movies using countvectorizer
3D Deep Learning with Python, Published by Packt
40 Algorithms Every Programmer Should Know, published by Packt
500 AI Machine learning Deep learning Computer vision NLP Projects with code
This is a collection of useful repositories providing resources for learning. Feel free to contribute here
I am trying to describe complex matters in simple doodles!
A multi-object tracking component. Works in the conditions where identification and classical object trackers don't (e.g. shaky/unstable camera footage, occlusions, motion blur, covered faces, etc.). Works on any object despite their nature.
Advanced Deep Learning with Keras, published by Packt
This repository contains implementation of different AI algorithms, based on the 4th edition of amazing AI Book, Artificial Intelligence A Modern Approach
Exercises for the book Artificial Intelligence: A Modern Approach
Common Lisp implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Project materials for RNN segment of AIND nanodegree
TIME SERIES ANALYSIS : Forecasted number of passengers for next 10 years of airlines using ARIMA model in python
Jupyter notebooks associated with the Algorithms for Optimization textbook
Algorithm Part 1 from Coursera
AlgorithmPart2 from Cousera by Princeton University
Collection of various algorithms implemented in R.
Coursera's Computer Science: Algorithms, Theory, and Machines by Princeton
Algorithms, 4th edition textbook code and libraries
Everything about programming!!
Classification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at som
Self-study on Larry Wasserman's "All of Statistics"