Practical Deep Learning with Tensorflow 2 and Keras

**UPDATED: Now using Tensorflow 2. Please post in Q&A if you have any trouble. I'm here to help**

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 and Tensorflow 2-- 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. 

I provide unmatched support. All questions answered within 24 hours. Try me and see ...  =]

What will I learn? 

• Basics of machine learning with minimal math

• A specialized but optional mathematics heavy talk that explains all the inner working of machine learning and deep learning 

• Applying machine learning principles to solve a real-world case study that includes pre-processing and getting your data into the proper shape. (This case study comes from a real research work I have carried out recently)

• Understand the often problematic shape issue that makes machine learning difficult to apply in real life 

• Learn the details of ConvNets and graph-based machine learning models such as Residual Connections and Google's Inception Module 

• Use Keras's functional API to create powerful models that will help you move way beyond the contents covered in this course 

• Learn how to use Google's GPUs to speed up your experiments for free

• Tips on avoiding mistakes made by new-comers to the field and the best practices to get you to your goal with minimal effort 

About the instructor: 

• Teacher and researcher by profession

• PhD in Security and a PostDoc from Max Planck Institute for Software Systems, Germany

• 20+ years of working with computers and 17+ years of teaching experience 

• 5+ years of working extensively with deep learning. I worked with almost all the modern tools as soon as they were released

Target Audience:

Anyone who:

• Wants to learn machine learning (this course is a soft introduction)  

• Knows machine learning and wants to learn deep learning (this course focuses on deep learning) 

• Knows deep learning but needs help applying their knowledge in practice (this is a very applied course) 

• Comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples) 

• Is a researcher or educator working in machine learning and wants to move from theory to practice

What you need to know:

• Python basics (installation, if, loops, lists) - Everything else will be covered in the course

• No machine learning background is assumed (but we keep the theory to a minimum)