Download Data Science: Deep Learning and Neural Networks in Python

What you would learn in Data Science: Deep Learning and Neural Networks in Python course?

This course will help you build your first Artificial Neural Network using advanced deep-learning methods. In the previous lesson on logistic regression, we will take this fundamental building block and construct complete non-linear neural systems from the beginning with Python and Numpy. The materials used in this course are free.

We extend the prior classifier model using binary data to encompass multiple classes with the help of the softmax function. In addition, we create the required training method known as " backpropagation" by using the first principles. I'll show you how you can code backpropagation using Numpy in two steps: first, "the slow way," and afterward, "the fast way" using Numpy features.

The next step is implementing the neural network with Google's brand-new TensorFlow library.

It is recommended to take this course if you're looking to begin your journey to becoming a master of deep learning or are fascinated by the field of machine learning and the sciences of data all over the world. We will go beyond the basic models, such as logistic and linear regression. I will teach you a technique that automatically learns new features.

This course gives you numerous examples of practical use to help you understand how deep learning can be applied to anything. Through this course, we'll work on an assignment for the course, which will help you determine the actions of users on a site based on user information such as whether or not the user is using a mobile device and the number of items they looked at, the time they spent on your site and whether or not they're returning customers and the time of time they visited.

A final project at the end of the course will show you how to use deep learning to improve facial expression detection. Imagine being in a position to predict the emotions of someone by looking at a photograph!

After you've got to grips with the basics, I give an overview of some of the latest advancements in neural networks, namely slightly altered architectures and the applications they can be used to do.

Course Content:

• Find out what Deep Learning is and how it does its work (not just a few diagrams or magic black box code)
• Learn the process of creating a neural network formed from the simplest components (the neuron)
• Make a neural net by hand in Python and NumPy.
• Make a neural network by using Google's TensorFlow
• Define the various types of neural networks, as well as the various types of problems they're employed for.
• Derive the rule of backpropagation from fundamental principles
• Create a neural system with an output with more than two classes with softmax
• Explain the various terms that are related to neural networks, including "activation," "backpropagation," and "feedforward."
• Install TensorFlow

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