Hands-On Keras for Machine Learning Engineers
What you'll learn
- 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
- An in-depth introduction to the Keras deep learning library
- How to develop and evaluate neural network models end-to-end
- How to serialize your models to disk
- A basic understanding of programming in Python
- Familiarity with the machine learning process
** Mike's courses are popular with many of our clients." Josh Gordon, Developer Advocate, Google **
"This is well developed with an appropriate level of animation and illustration." - Bruce
"Very good course for somebody who already has pretty good foundation in machine learning." - Il-Hyung Cho
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.
Who is this course for?
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 brieﬂy. With all of this in mind, this is an entry level course on the Keras library.
What are you going to Learn?
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 overﬁtting 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!
Who this course is for:
- If you want or need to learn Keras for you deep learning projects then this course is for you
- If you want to become a machine learning engineer then this course is for you
- If you want something beyond the typical lecture style course then this course is for you
I'm the founder of LogikBot. I've worked at Microsoft and Uber. I helped design courses for Microsoft's Data Science Certifications. If you're interested in machine learning, I can help.
I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman.
Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space.
Experience, education and passion
I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car.
Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT).
Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.