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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.
- How to do random shifts in image features using Python.
- How to do random flips in image features using Python.
- How to do augmentation in image features using Python.
Learn_By_Example_Image_Augmentation_Part_2
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Learn_By_Example_Image_Augmentation_Part_1:
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Support SETScholars for Free End-to-End Applied Machine Learning and Data Science Projects & Recipes by becoming a member of WA Center For Applied Machine Learning and Data Science (WACAMLDS). Membership fee only $1.75 per month (on annual plan) and you will get access to 425+ end-to-end Python & R Projects.
Western Australian Center for Applied Machine Learning & Data Science – Membership
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