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Welcome to Hands-On Keras for Machine Learning Engineers. This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and how to use it to develop and evaluate deep learning models.
There are two top numerical platforms for developing deep learning models, they are Theano developed by the University of Montreal and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super simple to use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models.
My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 55 on Udemy. If you're interested in learning what the real-world is really like then you're in good hands.
This course is for developers, machine learning engineers and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Basic concepts like cross-validation and one hot encoding used in lessons and projects are described, but only brieï¬‚y. With all of this in mind, this is an entry level course on the Keras library.
How to develop and evaluate neural network models end-to-end.
How to use more advanced techniques required for developing state-of-the-art deep learning models.
How to build larger models for image and text data.
How to use advanced image augmentation techniques in order to lift model performance.
How to get help with deep learning in Python.
The anatomy of a Keras model.
Evaluate the Performance of a deep learning Keras model.
Build end-to end regression and classification models in Keras.
How to use checkpointing to save the best model run.
How to reduce overï¬tting With Dropout Regularization.
How to enhance performance with Learning Rate Schedules.
Work through a crash course on Convolutional Neural Networks.
This course is a hands on-guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you'll get little out of it.
In the applied space machine learning is programming and programming is a hands on-sport.
Thank you for your interest in Hands-On Keras for Machine Learning Engineers.
Let's get started!