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The Multilayer Artificial Neural Network Course with Python

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back-propagation, feed-forward network, TensorFlow, Batch Normalization, Dropout, Pandas, Numpy, Matplotlib and so on.

Interested in the field of Deep learning? Then this course is for you!

What you’ll learn

  • multilayer neural network.
  • Learn to use NumPy for Numerical Data.
  • Learn to use Matplotlib for Python Plotting.
  • Pandas.
  • Neural Networks.
  • Overfitting.
  • Dropout.
  • Batch Normalization.
  • Multilayer Perceptron (MLP).
  • Apply Neural Networks in practice.
  • Tensorflow.

Course Content

  • Introduction –> 3 lectures • 5min.
  • Fundamental Neural Network –> 16 lectures • 3hr 23min.
  • Modelling Neural Network –> 10 lectures • 2hr 32min.
  • Classifying handwritten digits –> 7 lectures • 2hr.
  • Thank you –> 1 lecture • 1min.

The Multilayer Artificial Neural Network Course with Python

Requirements

Interested in the field of Deep learning? Then this course is for you!

This course has been designed to share my knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

I will walk you step by step into the world of artificial neural networks.

 

This course is fun and exciting, but at the same time, we dive deep into the artificial neural network. It is structured the following way:

  • Section 1: Introduction.
  • Section 2: Fundamental Neural Network
  • Section 3: Modelling neural networks
  • Section 4: Classifying Handwritten digits

There are lots of tools that we will cover in this course. These tools include TensorFlow, back-propagation, feed-forward network, and so on. A lot of other online courses did not cover back-propagation and this is a huge MISTAKE as back-propagation is an important topic. This course will not only cover back-propagation in theory but also implement it in the project. So you will have a deep understanding of back-propagation. You can empress your potential employer by showing the project with back-propagation.

 

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are three big projects and some small projects to practice what you have learned throughout the course. These projects are listed below:

  • Handwritten Digit.
  • Birth weights
  • MNIST

Become an artificial neural network guru today! I will see you inside the course!

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