Neural Network in Machine Learning Example | ML in Robotics Course | Lesson 9

🌟 Contents 🌟
💎 (00:00​​) Introduction
💎 (00:25​​) Overview of the Lesson
💎 (01:03​​) Requirements to follow the lesson
💎 (01:49​​) Implementation of the neural network using Python and TensorFlow for a multi-class classification problem
💎 (33:07​​) Result of the Implementation
💎 (35:44​​) Summary of the lesson

In the previous lesson, we understood the building blocks of a neural network, were introduced to different activation functions, understood the difference between shallow and deep neural networks, and became ready to use our software tools to develop a neural network.

In this lesson, we will:
- Work on a multi-class classification problem where a robot is expected to distinguish between dogs and cars and,
- Implement neural networks using Python and TensorFlow

Requirements:
- Python ‘pillow’ library (can be installed using conda)
- Python ‘pandas’ library (can be installed using conda too)
- Download the tutorial files and save them in a directory
- Run Spyder IDE and open the project

References (for datasets):

The code for this project can be accessed at the link below (Lesson 9 code):

Note that for your convenience, all lessons are organized into a playlist.

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