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Data Science: CNN & OpenCV: Breast Cancer Detection

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Partner: Udemy
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Description: If you want to learn the process to detect whether a person is suffering breast cancer using whole mount slide images of positive and negative Invasive Ductal Carcinoma (IDC) with the help of AI and Machine Learning algorithms then this course is for you.In this course I will cover, how to build a model to predict whether a patch of a slide image shows presence of breast cancer cells with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a deep learning model using Tensorflow, CNN, OpenCV and Python.This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like ResNet50 and at the same time creating a CNN model architecture from scratch, Model Checkpoints, model building and evaluation. Then using the trained model to detect the presence of breast cancer.I have split and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1  :  Project Overview.Task 2  :  Introduction to Google Colab.Task 3  :  Understanding the project folder structure.Task 4  :  Understanding the dataset and the folder structure.Task 5  :  Setting up the project in Google Colab_Part 1Task 6  :  Setting up the project in Google Colab_Part 2Task 7  :  About Config and Create_Dataset FileTask 8  :  Importing the Libraries.Task 9  :  Plotting the count of data against each class in each directoryTask 10  :  Plotting some samples from both the classesTask 11 :  Creating a common method to get the number of files from a directoryTask 12 :  Defining a method to plot training and validation accuracy and lossTask 13 :  Calculating the class weights in
Category: Development > Data Science > Deep Learning
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Price: 19.99
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Source: Impact
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