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ai_for_business_curriculum

This is the curriculum for the "AI for Business" Course By Siraj Raval on Youtube

ai_for_healthcare

This is the code for "AI for Healthcare" By Siraj Raval on Youtube

chest-x-ray-pneumonia-detection-with-keras-cnn

The aim of this kernel is to develop a robust deep learning model which classifies whether a chest X- ray image proves pneumonia or not. Finally the network classifies successfully the 87% of the test data. Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

classify-human-diseases-using-deeplearning

Automated methods to detect and classify human diseases from medical images, using Deep Neural Networks

cnn_medium.

A simple binary classifier to predict if the given image is a cat or a dog.

codesnippets

Code Snippets Repository for all my Content

colorhighlighter

ColorHighlighter - is a plugin for the Sublime text 2 and 3, which underlays selected hexadecimal colorcodes (like "#FFFFFF", "rgb(255,255,255)", "white", etc.) with their real color. Also, plugin adds color picker to easily modify colors. Documentation: https://monnoroch.github.io/ColorHighlighter.

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

cpython

The Python programming language

densenetkeras

A Keras implemention of DenseNet, will be further be modified to classify medical images.

gapminder

Excerpt from the Gapminder data, as an R data package and in plain text delimited form

gensim

Topic Modelling for Humans

jupyter_contrib_nbextensions

A collection of various notebook extensions for Jupyter

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

mnist

Python utilities to download and parse the MNIST dataset

one-time-scrapers

requests/beautiful soup based one time web scrapers

requests

A simple, yet elegant, HTTP library.

scikit-learn

scikit-learn: machine learning in Python

solidity

Solidity, the Smart Contract Programming Language

web_scraping