TOPICS COVERED IN THIS LECTURE
This lecture covers Deep Neural Networks, data Scaling, One-Hot Encoding, and using Training And Validation Sets to train a neural network. My Python code uses TensorFlow to build and train a neural network, and Pandas for data processing.
The great thing about machine learning is that there are always a ton of challenges out there. Governments, companies, and research labs are constantly introducing cool and complex new problems for computers to solve.
As a machine learning developer, you will never be bored 😉
Here is a classic machine learning challenge I’d like to share with you. It’s called ‘MNIST’ and uses the following training data:
Your goal is to build an app that can read these handwritten digits, and correctly associate each one with the digit value it represents.
So basically, what you’re doing here is Optical Character Recognition (OCR) on handwriting. Something that software developers have struggled with for decades.
But did you know you can easily solve the challenge in code?
Watch the lecture and I’ll show you how to build a Python app that uses TensorFlow to train a deep neural network to recognize these handwritten digits and correctly transcribe them to text.
– What is machine learning?
– Why use Python and TensorFlow?
– Setting up Python in Visual Studio
– The challenge: recognizing handwritten digits
– Introduction to neural networks
– The neural network we’re going to use
– Python code walkthrough
– Questions & answers
‘Good content, thanks!’