Practical Deep learning With Keras
Master Practical Neural Networks and Deep Learning With the Keras Framework in Python
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1
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Segment - 01 - Introduction
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Segment - 02 - Why AI and Deep Learning?
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Segment - 03 - Get Started with the Python Data Science Environment: Anaconda
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Segment - 04 - Anaconda for Mac Users
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Segment - 05 - The iPython Environment
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2
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Segment - 06 - Python Packages for Data Science
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Segment - 07 - Introduction to Numpy
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Segment - 08 - Create Numpy Arrays
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Segment - 09 - Numpy Operations
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Segment - 10 - Numpy for Basic Matrix Arithmetic
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Segment - 11 - Introduction to Pandas
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Segment - 12 - Read in Data from CSV
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Segment - 13 - Read in Data from Excel
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Segment - 14 - Basic Data Cleaning
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3
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Segment - 15 - What is Keras?
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Segment - 16 - Keras Installation-Windows
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Segment - 17 - Keras Installation on Mac OS
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4
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Segment - 18 - What is Machine Learning?
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5
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Segment - 19 - Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
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Segment - 20 - Activation Functions
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Segment - 21 - Multi Layer Perceptron (MLP) with Keras
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Segment - 22 - What is Backpropagation?
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Segment - 23 - Keras MLP for Binary Classification
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Segment - 24 - Accuracy Assessment for Binary Classification
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Segment - 25 - Keras MLP for Multiclass Classification
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Segment - 26 - Keras MLP for Regression
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6
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Segment - 27 - What is Unsupervised Learning?
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Segment - 28 - Autoencoders for Unsupervised Classification
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Segment - 29 - Autoencoders in Keras (Sparsity Constraints)
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Segment - 30 - Autoencoders in Keras (Simple)
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7
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Segment - 31 - DNN Classifier with Keras
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Segment - 32 - DNN Classifier with Keras-Example 2
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8
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Segment - 33 - Introduction to CNN
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Segment - 34 - CNN Workflow for Keras
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Segment - 35 - CNN with Keras
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Segment - 36 - CNN on Image Data with Keras-Part 1
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Segment - 37 - CNN on Image Data with Keras-Part 2