Debasish Mahana's repositories
python-for-data-analysis
An introduction to data science using Python and Pandas with Jupyter notebooks.
stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
tools
Codelabs management & hosting tools
Hello-Morse-OpenCV
Morse Code detection with eyes using Computer Vision
DeepSpeech
A TensorFlow implementation of Baidu's DeepSpeech architecture
TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners with Latest APIs
iot-devkit-samples
Intel System Studio IoT Edition project code samples using the Intel® IoT Developer Kit libraries.
tensorlayer
TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
Web-Development_Intern-Theory
Web development is a broad term for the work involved in developing a web site for the Internet (World Wide Web) or an intranet (a private network). Web development can range from developing the simplest static single page of plain text to the most complex web-based internet applications (or just 'web apps') electronic businesses, and social network services. A more comprehensive list of tasks to which web development commonly refers, may include web engineering, web design, web content development, client liaison, client-side/server-side scripting, web server and network security configuration, and e-commerce development. Among web professionals, "web development" usually refers to the main non-design aspects of building web sites: writing markup and coding. Most recently Web development has come to mean the creation of content management systems or CMS. These CMS can be made from scratch, proprietary or open source. In broad terms the CMS acts as middleware between the database and the user through the browser. A principle benefit of a CMS is that it allows non-technical people to make changes to their web site without having technical knowledge.
Data-Structure-And-Algorithm
Data Structure and Algorithms Tutorial. Data Structures are the programmatic way of storing data so that data can be used efficiently. Almost every enterprise application uses various types of data structures in one or the other way.
Hotel-Reservation-System
A console based java application. The main feauture of the app is in it's simplicity and use of core OOP feautures like Abstraction, Polymorphism, Encapsulation and Inheritance. The detailed description of parameters used are described below. One can modify it to suit their need. The hotel has three categories of rooms: Super Delux, Delux and Luxury. Only the Delux and Super Delux have wi-fi facility in the rooms. Delux and Luxury rooms may have either single or double occupancy. Each rooms has a a default room rate that is adjustable. The system changes the status of room to “occupied” when it is reserved. There is a facility to cancel the reservation also. The booking is done for some number of days. When a room is reserved, the room charge (room rate * no. of days) is displayed. The system also shows the status of rooms (whether occupied or vacant) in each category. The program displays a menu of alternatives to: Indicate the room type that needs to be booked Indicate the occpancy (single/double) in that category. If the rooms in a particular category are full, your program asks the person if it is acceptable to be placed in the other category. If yes, appropriate room is assigned.
Design_and_Analysis_of_Algorithm
This course will cover basic concepts in the design and analysis of algorithms. Asymptotic complexity, O() notation. Sorting and search. Algorithms on graphs: exploration, connectivity, shortest paths, directed acyclic graphs, spanning trees. Design techniques: divide and conquer, greedy, dynamic programming.Introduction to fundamental techniques for designing and analyzing algorithms, including asymptotic analysis; divide-and-conquer algorithms and recurrences; greedy algorithms; data structures; dynamic programming; graph algorithms; and randomized algorithms.
machine-learning-for-beginners
Start writing machine learning code in just 10 hours
handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Source-Code-from-Tutorials
Here is the source code from all of my tutorials.
pycon-ds-2018
Introduction to Python for Data Science for PyCon 2018
HydPy-meetups
All the code from hands on sessions will be shared here.
ML-course
A machine learning course using Python, Jupyter Notebooks, and OpenML
data_sciences_campaign
【数据科学家系列课程】
Spider
Python website crawler.
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
datacamp-community-tutorials
Tutorials for DataCamp (www.datacamp.com)
python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource
courses-intro-to-sql
DataCamp's course: Intro to SQL for Data Science
thesemicolon
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
PyMLSlides
Slides for my machine learning course based on Sebastian Raschka's Python Machine Learning book
Python-Design-Patterns
Python Design Patterns
sklearn-classification
Data Science Notebook on a Classification Task, using sklearn and Tensorflow.