Size: 490.85 MB
Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
What you’ll learn
- Apply momentum to backpropagation to train neural networks
Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
Understand the basic building blocks of Theano
- Build a neural network in Theano
- Understand the basic building blocks of TensorFlow
- Build a neural network in TensorFlow
- Build a neural network that performs well on the MNIST dataset
- Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
- Understand and implement dropout regularization in Theano and TensorFlow
- Understand and implement batch normalization in Theano and Tensorflow
- Write a neural network using Keras
- Write a neural network using PyTorch
- Write a neural network using CNTK
- Write a neural network using MXNet
- Be comfortable with Python, Numpy, and Matplotlib. Install Theano and TensorFlow.
- If you do not yet know about gradient descent, backprop, and softmax, take my earlier course, deep learning in Python, and then return to this course.
DescriptionThis course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you. You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.
- linear algebra
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- neural networks and backpropagation
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
- Students and professionals who want to deepen their machine learning knowledge
- Data scientists who want to learn more about deep learning
- Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
- Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first