Deep learning using Tensorflow Lite on Raspberry Pi | Udemy

Deep learning using Tensorflow Lite on Raspberry Pi | Udemy
English | Size: 3.27 GB
Genre: eLearning

Power up your Embedded projects with Artificial Intelligence in Python using TF Lite

What you’ll learn
Build your own AI Projects
Raspberry Pi 4 based Robot for Computer Vision
Neural Network to classify your Voice
Custom Convolution Network Creation

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 :

Non-Linear Function Approximation

Visual Calculator

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


Motivated mind for a huge programming Project


Before buying take a look into this course GitHub repository

Who this course is for:
Electrical Engineers
Artificial Intelligence Enthusiasts

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