Practical Deep Learning with Tensorflow 2.x and Keras

Learn to apply Tensorflow to YOUR problems. Follow a complete pipeline including pre-processing and training for ML.


What you'll learn
Be able to run deep learning models with Keras on Tensorflow 2 backend
Run Deep Neural Networks on a real-world scientific protein dataset
Understand how to feed own data to deep learning models (i.e. handling the notorious shape mismatch issue)
Understand Deep Learning, CNN, dropout, functional API with minimal of math
Understand and use Keras' functional API to create models with multiple inputs and outputs
Learn how to do Transfer Learning practically
Stunning SUPPORT. I answer questions on the same day.

Requirements
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

Description
**UPDATED: Now using Tensorflow 2. Please post in Q&A if you have any trouble. I'm here to help**
**UPDATED 11-2021: Added a section on Practical Transfer Learning**
TensorFlow is by far, the most popular library for deep learning. Backed by Google, it is a solid investment of your time and efforts if you want to succeed in the area of machine learning and AI. The issue most people face is that getting started with Tensorflow guides usually delve too deeply into unnecessary mathematics.
That is where this course comes in. While some theory is important, a lot of it is just not needed when you're just getting started!
This course is for you
if you are new to Machine Learning
but want to
learn it without all the complicated 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 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.x
– 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
Functional API of Keras / Tensorflow 2.x
Transfer Learning
In this course, we explain concepts using not only toy datasets but also a real-world dataset from the bioinformatics domain. While you may not be interested in this particular domain, you would still learn a lot of important concepts that are involved in taking data from the real world and feeding it to ML models. This is the aspect of ML that is missing from almost all courses available on the internet today! Doing this would mean that
you would be able to solve problems of your own industry after finishing this course
.
All with only a few lines of code. All the examples used in the course come with
a starter code that 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. Do checkout the preview lectures on this page to get a better feel of the teaching style used in this course and how it can help you learn quickly.
I provide unmatched support. All questions are answered within 24 hours. Try me and see … =]
Who this course is for:
Anyone who wants to learn machine learning (this course is a soft introduction)
Anyone who knows machine learning and wants to learn deep learning (this course focuses on deep learning)
Anyone who knows deep learning but needs help applying their knowledge in practice (this is a very applied course)
Anyone who is 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)
Anyone who is a researcher or educator working in machine learning and wants to move from theory to practice

More Info