Deep Learning: Convolutional Neural Networks in Python Tutorials

Deep Learning: Convolutional Neural Networks in Python Tutorials

Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow

What you’ll learn

Deep Learning: Convolutional Neural Networks in Python Tutorials

  • Understand convolution
  • Learn how convolution can be applied to audio effects
  • Understand how convolution can be applied to image effects
  • Implement Gaussian blur and edge detection in code
  • Implement a simple echo effect in code
  • Understand how convolution helps image classification
  • Understand and explain the architecture of a convolutional neural network (CNN)
  • Implement a convolutional neural network in Theano
  • Implement a convolutional neural network in TensorFlow


  • Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow
  • Learn about backpropagation from Deep Learning in Python part 1
  • Learn about Theano and TensorFlow implementations of Neural Networks from Deep Learning part 2


This is the 3rd part of my Data Science and Machine Learning series on Deep Learning in Python. 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 for computer vision using convolutional neural networks.

In this course, we are going to up the ante and look at the StreetView House Number (SVHN)
I’m going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I’m going to show you how to build filters for image effects, like the Gaussian blur and edge detection.

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.

Suggested Prerequisites:

  • calculus (taking derivatives)
  • matrix addition, multiplication
  • 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 or 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.

  • 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)

Who this course is for:

  • Students and professional computer scientists
  • Software engineers
  • Data scientists who work on computer vision tasks
  • Those who want to apply deep learning to images
  • Those who want to expand their knowledge of deep learning past vanilla deep networks
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