Description
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
- Python3
- Motivated mind for a huge programming Project
Before buying take a look into this course GitHub repository
Who this course is for:
- Developers
- Electrical Engineers
- Artificial Intelligence Enthusiasts
Requirements
- Basic Electronics Understanding
- Basic Python Programming
- Hardware : Raspberry pi 4
- Hardware : 12V Power Bank
- Hardware : Raspberry PI Camera V2
- Hardware : 2 LEDs ( Red and Green )
- Hardware : Bread Board
- Hardware : RPI 4 Fan
- Hardware : 3D printed Parts
- Hardware : Jumper Wires
Last Updated 9/2022
Download Links
Direct Download
Deep learning using Tensorflow Lite on Raspberry Pi.zip (3.3 GB) | Mirror
Torrent Download
Deep learning using Tensorflow Lite on Raspberry Pi.torrent (73 KB) | Mirror
