Deep Learning: Convolutional Neural Networks in Python

Course details

This is the 3rd part in my Data Science and Machine Learningseries on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU.

This course is all about how to use deep learning forcomputer vision usingconvolutional neural networks. These are the state of the art when it comes toimage classification and they beat vanilla deep networks at tasks like MNIST.

In this course we are going to up the ante and look at theStreetView House Number (SVHN)dataset - which uses larger color images at various angles- so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!

Becauseconvolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such asmodeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied toaudio, like the echo effect, and I'm going to show you how to build filters forimage effects,like theGaussian blurandedge detection.

We will also do somebiology and talk about how convolutional neural networks have been inspired by theanimal visual cortex.

After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2)with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about"seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you wantmorethan just a superficial look at machine learning models, this course is for you.

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: cnn_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES /KNOWLEDGEYOUARE ASSUMEDTOHAVE:

• calculus
• linear algebra
• probability
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file
• Can write a feedforward neural network in Theano and TensorFlow

TIPS (for getting through the course):

• 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.

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• 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)