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-100daysofmlcode-ocr-invoice-recognition

In 100 days i will an OCR invoice recognition software

-first-steps-with-tensorflow

First Steps with TensorFlow

business-intelligence-with-power-bi

Go from absolute beginner in Power BI to getting hired as a confident and effective Analyst. Learn Power BI (+ much more) by analyzing real-world datasets and building enterprise-level projects.

clinicalcodes

An online clinical codes repository to improve validity and reproducability of medical database research

cnn-classifier-for-cats-and-dogs

Building a convolutional neural network with tensorflow to classify cats and dogs

cnn-with-deep-learning-tensorflow

building a convolutional neural network with tensorflow on the MNIST dataset

complete-data-science-machine-learning-with-pytorch

I will teach you step by step on to build machine learning model using pytorch

constantinembufung

Config files for my GitHub profile.

data-analysis-with-python

# Data Analyis with Python Python is a great lanague for doing data analysis. Python have fantastic packages sucha s Pandas, Numpy and Matplotlib that give you a single place to analyse data and do visualization. We'll use Pandas to analyze data on video game reviews from IGN one of the most popular video review site. We are going to use Python 3.5 and google colab running jupyter notebook 4 # Import Data with Pandas our file is a csv file, comma-separated values

deep_learning_with_keras

deep_learning_with_tensorflow_keras

In this tutorial series, we will learn the basics of TensorFlow, neural networks and deep learning: We will learn: What is Tensorflow and keras An intriduction to neural networks what the perception and multi-perception are real world examples: handwritten digits recognition

flask

A microframework based on Werkzeug, Jinja2 and good intentions

idota

iDOTA is AI-based retinal screening application that is developed at the Douala School of AI Research Lab(DSAIRLab) to easily diagnose and prevent blindness in Cameroon. According to the WHO, the number of people of all ages visually impaired is estimated to be 285 million, of whom 39 million are blind. People 50 years and older are 82% of all blind. The major causes of visual impairment are uncorrected refractive errors (43%) and cataract (33%); the first cause of blindness is cataract (51%).

jekyll-bootstrap

this is the first bootstrap 4 beta project

jupyter_demonstrations

Random jupyter notebooks on data analysis and machine learning

live-loss-plot-example

Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training! A live training loss plot in Jupyter Notebook for Keras, PyTorch and other frameworks. An open source Python package by Piotr Migdał et al. When used with Keras, Live Loss Plot is a simple callback function.

lstm_image_caption

Applications of LSTM - Image caption Generation. For generating captions for images, we will use a popular dataset for image captioning tasks known as Microsoft Common Objects in Context (MS-COCO). We will �rst process images from the dataset (MS-COCO) to obtain an encoding of the images with a pretrained Convolutional Neural Network (CNN), which is already good at classifying images. The CNN will take a �xed-size image as the input and output the class the image belongs to (for example, cat, dog, bus, and tree). Using this CNN, we can obtain compressed encoded vectors describing images. Then we will process the captions of the images to learn the word embeddings of the words found in captions. We can also use pretrained word vectors for this task. Finally, having obtained both the image and word encodings, we will feed them into an LSTM and train it on the images and their respective captions.

movie-reviews-with-deep-learning-tensorflow

Movie Reviews with Deep Learning Tensorflow

multiclass-classification-with-tensorflow

mysql-country-list

mysql insert country list into database

neural-network-on-mnist-dataset

Lets implement a neural network that is able to classify digits. For this task, we will be using the famous MNIST dataset made available at http://yann.lecun.com/exdb/mnist/

predicting-customer-churn

A common problem across businesses in many industries is that of customer churn. Businesses often have to invest substantial amounts attracting new clients, so every time a client leaves it represents a significant investment lost. Both time and effort then need to be channelled into replacing them. Being able to predict when a client is likely to leave and offer them incentives to stay can offer huge savings to a business. This is the essence of customer churn prediction; how can we quantify if and when a customer is likely to churn?

python-for-finance

Today i am starting a tutorial on using python for finance- we are going to pull stock data from yahoo finance and do data analysis and backtesting using zipline

python-for-finance-and-backtesting

r

Some applications about R Programmig Language

spark

Mirror of Apache Spark

telecom-customers-churn-prediction

test

This is test for a new project

text-generation-using-neural-network-lstm

we going to used LSTM for text generation. Credit to Thushan Ganegedara. You can read his book on packt.com on NLP using Tensorflow

titanic-data-analysis-with-python

Titanic Data Analysis with python