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Learn_By_Example_Image_Augmentation_Part_1

By on Monday, March 30, 2020

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Learn_By_Example_Image_Augmentation_Part_1.

What should I learn from this recipe?

You will learn:

  • How to code a keras and tensorflow model in Python.
  • How to setup a sequential deep learning model in Python.
  • How to setup Early Stopping in a Deep Learning Model in Keras.
  • How to split train and test datasets in a Deep Leaning Model in Keras.
  • How to incorporate Multiple Layers in a Deep Learning model.
  • How to reduce overfitting in a Deep Learning model.
  • How to test different OPTIMIZERs and Epoch Sizes in a Deep Learning model.
  • How to setup an experiment in a Deep Learning model.
  • How to setup CNN layers in Keras for image classification.
  • How to classify images using CNN layers in Keras: An application of MNIST Dataset
  • How to create simulated data using scikit-learn.
  • How to create training and testing dataset using scikit-learn.
  • How to train a tensorflow and keras model.
  • How to report confusion matrix.
  • How to plot MNIST dataset in Python.
  • How to standarise image features in Python using MNIST dataset.
  • How to do whitening transformation in image features using Python.
  • How to do random rotation in image features using Python.

 

Learn_By_Example_Image_Augmentation_Part_1

Learn_By_Example_Image_Augmentation_Part_1:



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How to setup an experiment in a Deep Learning model

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