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2019_caicedo_cytometrya

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

aida-dataset-register.github.io

Information on datasets shared on the AIDA data hub.

aidadatahub.github.io

AIDA Data Hub website

data_manipulation_mynotes

My Notes: Data Manipulation with Numpy and Pandas

data_preprocessing_mynotes

My Notes: Data Preprocessing for Outliers, Missing Values and Data Standardization

deep-learning-for-medical-applications

Deep Learning Papers on Medical Image Analysis

diabetes_prediction_pyladies

dltk

Deep Learning Toolkit for Medical Image Analysis

dog_classification

eda_mynotes

My Notes: Exploratory Data Analysis and Visualization

introtopython

Files associated with our book Intro to Python for Computer Science and Data Science

machine_learning_mynotes

My Notes: Machine Learning for Data Science

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.

medical_image_analysis

nfl_ml_kaggle

predicting NFL play yardage using neural networks

nlp_lund

nlp_sentiment_analysis_and_classification

Project: Natural Language Processing for Sentiment Analysis and Classification Models

predicting_diabetes

PROJECT: Predicting Diabetes Using Machine Learning Models

python_programming_mynotes

My Notes: Basic Codes, Data Structures and Basic Functions for Python

stackoverflow

Findings from Stackoverflow 2017

statistics_for_data_science_mynotes

My Notes: Basic Statistics for Data Science

testing_session_duration

Project: Testing Session Duration for an Online Shopping Site

titanic_ml_kaggle

Kaggle Project: Predicting Which Passengers Survived the Titanic Shipwreck Using Machine Learning to Create Models

uppsala_uni_assignments