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Table of contents
Welcome to Deep Learning 101!
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Module 1: Introduction to Deep Learning
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Module 2: Building blocks of Deep Learning
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Module 3: Exercise - Let's build a simple Neural Network
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Module 4: Hyperparameters of Neural Networks
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- Download your hyperparameters cheat sheet
- All hyperparameters overview (7:15)
- Determining number of neurons and hidden layers (4:23)
- Loss function, optimizers and learning rate (4:47)
- Activation function, batch size and # of epochs (4:46)
- Weight initialization and regularization (2:20)
- How to set up hyperparameters in Keras (12:20)
- Questions
- Answers to questions
Module 5: Over/underfitting problem and solutions
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Module 6: Unstable Gradients
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Module 7: Computational time
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- Overview of solutions to long training times of Neural Networks (1:52)
- Normalizing your data (3:17)
- Using mini-batches (13:56)
- Selecting the right optimization algorithm (13:36)
- Pruning your network (1:49)
- Learning rate scheduling (7:57)
- How to set up each solution in Keras (10:52)
- Questions
- Answers to questions
Module 8: Exercise - Setting up neural networks
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Module 9: Model diagnosis and making improvements
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Module 10: Hyperparameter tuning
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Module 11: Exercise - Improving the model
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BONUS: RNNs and CNNs (coming soon)
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Final Project - Building a web app (coming soon)
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Congratulations on finishing the course!
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Continue learning...
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