What you’ll learn
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The theory and math underlying deep learning
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How to build artificial neural networks
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Architectures of feedforward and convolutional networks
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Building models in PyTorch
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The calculus and code of gradient descent
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Fine-tuning deep network models
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Learn Python from scratch (no prior coding experience necessary)
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How and why autoencoders work
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How to use transfer learning
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Improving model performance using regularization
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Optimizing weight initializations
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Understand image convolution using predefined and learned kernels
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Whether deep learning models are understandable or mysterious black-boxes!
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Using GPUs for deep learning (much faster than CPUs!)
Deal Score0
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