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.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
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