Deep Learning: Convolutional Neural Networks in Python

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

Deep Learning: Convolutional Neural Networks in Python
Deep Learning: Convolutional Neural Networks in Python

What you'll learn:

  • Understand convolution
  • Understand 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

 

Requirements:

  • Derivatives, matrix arithmetic, probability
  • You should know some basic Python coding with the Numpy Stack
 

Description:

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.

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 want more than just a superficial look at machine learning models, this course is for you.

 

 

Suggested Prerequisites:

  • calculus (taking derivatives)

  • matrix arithmetic

  • probability

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

 

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.

 

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • 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:

  • Adult learners who want to get into the field of data science and big data
  • Students who are thinking of pursuing machine learning or data science
  • Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python
  • People who know some machine learning but want to be able to relate it to artificial intelligence
  • People who are interested in bridging the gap between computational neuroscience and machine learning

Course Details:

  • 12 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

Deep Learning: Convolutional Neural Networks in Python udemy courses free download

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