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dl_ml_machinelearning6.867

My own notes, implementations, and musings for MIT's graduate course in machine learning, 6.867

effectivepytorch

PyTorch tutorials and best practices.

end2end-all-conv

End-to-end training for breast cancer diagnosis using deep all convolutional networks

fcm_m

Here are some variants of FCM clustering methods in matlab codes.

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

fundamentals-of-deep-learning-book

Code companion to the O'Reilly "Fundamentals of Deep Learning" book

gliomarad

Predict tumor genetics using CNN and radiomics features

gmic

BC_Detection_ An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow

Part I: Notebooks & Code "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow-3rd-edition

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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.

hello-world

hello-world1

# Starting GIthub

hila

Semantic Segmentation Transformer

hipt

Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)

inbreast

java-mass-detection

keras-io

Keras documentation, hosted live at keras.io

latest-news-classifier

Master in Data Science Final Project

learned-fourier-features

Code for the paper "Functional Regularization for Reinforcement Learning via Learned Fourier Features"

learnopencv

Learn OpenCV : C++ and Python Examples

lsmlib

Level Set Method Library

machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

machine-learning-collection

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

machine-learning_tutorials

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

machine_learning_basics

Plain python implementations of basic machine learning algorithms

mahmoods__ai-based-pathology

AI-based pathology predicts origins for cancers of unknown primary - Nature

mammographic-mass-cad-via-pseudo-color-mammogram-and-mask-r-cnn

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

mammography-models

Machine learning models for analyzing mammograms

mammomasses-project

Predict whether a mammogram mass is benign or malignant We'll be using the "mammographic masses" public dataset from the UCI repository (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass) This data contains 961 instances of masses detected in mammograms, and contains the following attributes: 1. BI-RADS assessment: 1 to 5 (ordinal) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives. we will apply several different supervised machine learning techniques to this data set, and see which one yields the highest accuracy as measured with K-Fold cross validation (K=10). we will apply: * Decision tree * Random forest * KNN * Naive Bayes * SVM * Logistic Regression * And, as a bonus challenge, a neural network using Keras.