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Course Deep Learning Neural Network Python Keras

Become A Champion In Deep Learning Through This Course

By ridhigrg |Email | Mar 25, 2019 | 21798 Views

There are techniques that are known to address specific issues when configuring and training a neural network that is available in modern deep learning libraries such as Keras.

In this crash course, you will discover how you can confidently get better performance from your deep learning models in seven days.

This is a big and important post. You might want to bookmark it.
Click Here to get started with this course.

This CrashCourse is for?
Before we get started, letâ??s make sure you are in the right place.

The list below provides some general guidelines as to who this course was designed for.
You need to know:
Your way around basic Python and NumPy.
The basics of Keras for deep learning.

You do NOT need to know:
How to be a math wiz!
How to be a deep learning expert!
This crash course will take you from a developer that knows a little deep learning to a developer who can get better performance on your deep learning project.

CrashCourse Overview
This crash course completes the course with 7 chapters.

You could complete one lesson per day (recommended) or complete all of the lessons in one day (hardcore). It really depends on the time you have available and your level of enthusiasm.

Below are seven lessons that will allow you to confidently improve the performance of your deep learning model:
Lesson 01: Better Deep Learning Framework
Lesson 02: Batch Size
Lesson 03: Learning Rate Schedule
Lesson 04: Batch Normalization
Lesson 05: Weight Regularization
Lesson 06: Adding Noise
Lesson 07: Early Stopping
Each lesson could take you 60 seconds or up to 30 minutes. Take your time and complete the lessons at your own pace.

You  will discover:
  • A framework that you can use to systematically diagnose and improve the performance of your deep learning model.
  • Batch size can be used to control the precision of the estimated error and the speed of learning during training.
  • Learning rate schedule can be used to fine tune the model weights during training.
  • Batch normalization can be used to dramatically accelerate the training process of neural network models.
  • Weight regularization will penalize models based on the size of the weights and reduce overfitting.
  • Adding noise will make the model more robust to differences in input and reduce overfitting
  • Early stopping will halt the training process at the right time and reduce overfitting.
  • This is just the beginning of your journey with deep learning performance improvement. Keep practicing and developing your skills.

Source: HOB
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