Practical Deep Learning with Keras and Python
Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training.
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:
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.
What will I learn?
About the instructor:
What you need to know:
Watch this first!FREE PREVIEW
About the InstructorFREE PREVIEW
Dive into Machine LearningFREE PREVIEW
Machine Learning Pipeline
Binary and Multi-class Classification
Recap and a Link to More Theory
Environment setup for Windows (and some issues with it)
Environment setup for Mac and Linux
Training and Testing
Problem Description and Data View
Pre-processing the Data
Loading Data and Getting the Shapes Right
Train, Test Split
Shapes in Depth (or how not to have headaches for days)
Basics and Rationale
CNN in Keras (or why Keras is better than your ML tool)
Pooling (and why it's not that important)
Dropout (and why you should always consider it)
Functional API for CNN
Saving and Loading Model Weights
SK learn first file
Course Resources Download
PhD, programmer, researcher, designer and teacher.
I have a PhD in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. I have been programming since early 2000 and have worked with many different languages, tools and platforms.
I have an extensive research experience with many state-of-the-art models to my name. My research in Android security has led to some major shifts in the Android permission model.
I love teaching and the most important reason I upload online is to make sure people can find my content.
"Practical Deep Learning with Keras and Python"
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