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Practical Deep Learning with Keras and Python

LInk : Practical Deep Learning with Keras and Python

Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training. About This Video Run deep learning ...

Practical Deep Learning with Keras and Python
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What you'll learn
  • Be able to run deep learning models with Keras on Tensorflow backend
  • Stunning SUPPORT. I answer questions on the same day.
  • Understand how to feed own data to deep learning models (i.e. handling the notorious shape mismatch issue)
  • Understand Deep Learning with minimal of math
  • Understand and code Convolutional Neural Networks as well as graph-based deep models involving residual connections and inception modules
  • Get tips on how to use Google's GPUs to speed up your experiments for free
  • Understand and use Keras' functional API to create models with multiple inputs and outputs
  • You should be able to use Python (if, while, lists. Everything else will be covered in the course)
  • NO prior knowledge of machine learning is assumed

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems.

In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras -- one of the easiest and most powerful machine learning tools out there.

You will start with a basic model of how machines learn and then move on to higher models such as:
  • Convolutional Neural Networks 
  • Residual Connections 
  • Inception Module
  • All with only a few lines of code. All the examples used in the course comes with starter code which will get you started and remove the grunt effort. The course also includes finished codes for the examples run in the videos so that you can see the end product should you ever get stuck.

There is also a real-time chat system in place for students who enroll in this course. With a free signup, you get access to real-time chat with myself and fellow students who are working to complete this course (or have completed the course before you). We plan on creating this network of like-minded machine learning experts who can help each other out and collaborate on exciting ideas together.
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