Deep learning using Tensorflow Lite on Raspberry Pi

Deep learning using Tensorflow Lite on Raspberry Pi, available at $64.99, has an average rating of 4.4, with 63 lectures, based on 11 reviews, and has 211 subscribers.

You will learn about Build your own AI Projects Raspberry Pi 4 based Robot for Computer Vision Neural Network to classify your Voice Custom Convolution Network Creation This course is ideal for individuals who are Developers or Electrical Engineers or Artificial Intelligence Enthusiasts It is particularly useful for Developers or Electrical Engineers or Artificial Intelligence Enthusiasts.

Enroll now: Deep learning using Tensorflow Lite on Raspberry Pi

Summary

Title: Deep learning using Tensorflow Lite on Raspberry Pi

Price: $64.99

Average Rating: 4.4

Number of Lectures: 63

Number of Published Lectures: 63

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Build your own AI Projects

Raspberry Pi 4 based Robot for Computer Vision

Neural Network to classify your Voice

Custom Convolution Network Creation

Who Should Attend

Developers

Electrical Engineers

Artificial Intelligence Enthusiasts

Target Audiences

Developers

Electrical Engineers

Artificial Intelligence Enthusiasts

Course Workflow:

This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data .

We will start with trigonometric functions approximation . In which we will generate random data and produce a model for Sin function approximation

Next is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classification

Another amazing project is focused on convolution network but the data is custom voice recordings . We will involve a little bit of electronics to show the output by controlling our multiple LEDs using own voice.

Unique learning point in this course is Post Quantization applied on Tensor flow models trained on Google Colab . Reducing size of models to 3 times and increasing inferencing speed up to 0.03 sec per input .

Sections  :

1. Non-Linear Function Approximation

2. Visual Calculator

3. Custom Voice Controlled Led

Outcomes After this Course : You can create

Deep Learning Projects on Embedded Hardware

Convert your models into Tensorflow Lite models

Speed up Inferencing on embedded devices

Post Quantization

Custom Data for Ai Projects

Hardware Optimized Neural Networks

Computer Vision projects with OPENCV

Deep Neural Networks with fast inferencing Speed

Hardware Requirements

Raspberry PI 4

12V Power Bank

2 LEDs ( Red and Green )

Jumper Wires

Bread Board

Raspberry PI Camera V2

RPI 4 Fan

3D printed Parts

Software Requirements

Python3

Motivated mind for a huge programming Project

-

Before buying take a look into this course GitHub repository

Course Curriculum

Chapter 1: Non Linear Trigonometric Functions Approximation

Lecture 1: How Nerual Networks Work

Lecture 2: Non-Linear Function Approximation Understanding

Lecture 3: Trigonometric Function Data Generation

Lecture 4: Data Splitting and Normalizing

Lecture 5: Deep Learning Model Creation

Lecture 6: Model Performance testing and Loss Understanding

Lecture 7: Mean Squared Error Graph Understanding

Lecture 8: Designing New Improved Model

Lecture 9: Model Performance comparisons and Saving

Lecture 10: Github Push after Section Completion

Lecture 11: Github repository and Resources

Chapter 2: Visual Calculator

Lecture 1: Join our free community

Lecture 2: Raspberry PI OS setup

Lecture 3: Construction of Hardware

Lecture 4: Data Strategy for this project

Lecture 5: Producing Custom Data

Lecture 6: Raspberry PI SSH Setup using Vscode

Lecture 7: Video Saving Script

Lecture 8: Data Videos Obtaining

Lecture 9: Understanding Frame Extraction Process

Lecture 10: Hough Circles Understanding

Lecture 11: Number Extraction from Circles

Lecture 12: Data Obtaining and Pre Processing

Lecture 13: Visualizing Data on Google Colab

Lecture 14: 3D printing parts Source

Lecture 15: Model Architecture

Lecture 16: Model Implementation and training

Lecture 17: Model Saving

Lecture 18: Testing Model

Lecture 19: Model Performance Matrices understanding

Lecture 20: Post Quantization

Lecture 21: TFLite Conversion

Lecture 22: TF Lite Model Testing

Lecture 23: Real Time Prediction Script

Lecture 24: Model Inferencing on recorded Data

Lecture 25: Defining Region of Interest

Lecture 26: Testing ROI Improvements

Lecture 27: Raspberry PI Model Inferencing Setup

Lecture 28: RPI Inferencing Testing

Lecture 29: Equation Building

Lecture 30: Number Detection Isolation

Lecture 31: Equation Computation

Lecture 32: Github Push

Chapter 3: Voice Controlled LEDs

Lecture 1: Understanding Wave Files

Lecture 2: Audio Recording Script

Lecture 3: Audio Conversion from Float to Integer

Lecture 4: Data Recording in batches

Lecture 5: Wave file to Binary Tensors

Lecture 6: Data Visualizations

Lecture 7: Spectrogram Conversion

Lecture 8: Data Pre-processing Pipelines

Lecture 9: Model definition and Dataset Splitting

Lecture 10: Model Architecture

Lecture 11: Discussion Model Parameters and Training Model

Lecture 12: Reviewing Training Results

Lecture 13: Model Performance Matrix

Lecture 14: Tensorflow Lite Conversion and prediction

Lecture 15: Input Audio stream pipeline building

Lecture 16: TfLite model predictions

Lecture 17: LED connections and Blink

Lecture 18: Raspberry Pi Model Predictions

Lecture 19: Real Time Audio LED Controlling

Lecture 20: Github Push

Instructors

Muhammad Luqman
Heavy Roboticist

Zaheer Ahmed
Computer Engineer

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 2 votes

5 stars: 7 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!