You’re going to learn the most popular library to build networks and machine learning algorithms.
In this hands-on, practical course, you will be working your way through with Python, Tensorflow, and Jupyter notebooks.
What you will learn:
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Basics of Tensorflow
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Artificial Neurons
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Feed Forward Neural Networks
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Activations and Softmax Output
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Gradient Descent
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Backpropagation
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Loss Function
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MSE
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Model Optimization
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Cross-Entropy
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Linear Regression
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Logistic Regression
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Convolutional Neural Networks (with examples)
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Text and Sequence Data
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Recurrent Neural Networks (with examples)
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Neural Style Transfer (in progress)
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