This course was created with the
course builder. Create your online course today.
Start now
Create your course
with
Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Deep Learning 101 with Python and Keras
Welcome to Deep Learning 101!
How does the course work? (4:00)
All resources
Module 1: Introduction to Deep Learning
Quick overview of Deep Learning (18:30)
Questions
Answers to questions
Module 2: Building blocks of Deep Learning
Forward pass in Neural Networks (18:32)
Backward pass in Neural Networks (15:59)
Questions
Answers to questions
Module 3: Exercise - Let's build a simple Neural Network
Setting up a virtual environment (8:19)
An alternative development environment
Importing and preparing the data (10:34)
Building the model (17:48)
Model evaluation (6:50)
Assignment
Module 4: Hyperparameters of Neural Networks
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
What is over/underfitting and how do we deal with it? (11:32)
L1/L2 Regularization (8:07)
Dropout Regularization (5:23)
Other techniques: data augmentation, early stopping (5:29)
How to set up each solution in Keras (8:43)
Questions
Answers to questions
Module 6: Unstable Gradients
What is vanishing/exploding gradients problem? (5:54)
Choosing the right initializer (3:40)
Non-saturating activation functions (5:24)
Batch normalization (13:10)
Gradient clipping (6:31)
How to set up each solution in Keras (9:54)
Questions
Answers to questions
Module 7: Computational time
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
Introduction to the data and the notebook (3:44)
Initializing the network (7:14)
Dealing with overfitting (7:12)
Learning rate scheduling (6:16)
Options for initializing the network (5:31)
Assignment
Module 9: Model diagnosis and making improvements
Model evaluation metrics (7:01)
Levels of performance (4:11)
Determining optimal performance level for your model (9:49)
Deciding on improvements to make (3:22)
Questions
Answers to questions
Module 10: Hyperparameter tuning
Hyperparameter tuning problem and common solutions (6:33)
Advanced hyperparameter tuning algorithms (7:19)
Questions
Answers to questions
Module 11: Exercise - Improving the model
Diagnosing the model (4:01)
Hyperparameter tuning (11:57)
Training the model further (2:44)
Assignment
BONUS: RNNs and CNNs
RNNs: Basics (15:45)
RNNs: LSTMS and GRU cells (10:06)
RNNs: Implementation using Keras (6:49)
CNNs: Basics (12:06)
CNNs: Calculating results in practice (14:19)
CNNs: Implementation using Keras (2:42)
Congratulations on finishing the course!
A word from your instructor (0:59)
Let me know what you thought about the course
Continue learning...
Hands-on Data Science: Complete your first portfolio project
Dropout Regularization
Complete and Continue
Discussion
4
comments
Load more
4 comments