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Deep learning using Tensorflow Lite on Raspberry Pi
Size :3.3 GB
Peers : Seeders : 0      Leechers : 0
Added : 2 years ago » by tutsnode » in Other
Language : English
Last Updated :6 months ago
Info_Hash :65836B57FDE0DF3BB21D6DC5DF238BAA79CD1C1D

Torrent File Contents

Deep learning using Tensorflow Lite on Raspberry Pi
  [TutsNode.net] - 2. Visual Calculator/26. z.mp4
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  TutsNode.net.txt
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  [TGx]Downloaded from torrentgalaxy.buzz .txt
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  [TutsNode.net] - 3. Audio Led Controlling/1. Resources for installations..html
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  [TutsNode.net] - 2. Visual Calculator/27. za.mp4
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  [TutsNode.net] - 1. Non Linear Trigonometric Functions Approximation/2. b.mp4
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  [TutsNode.net] - 1. Non Linear Trigonometric Functions Approximation/1. a.mp4
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  [TutsNode.net] - 1. Non Linear Trigonometric Functions Approximation/6. f.mp4
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Torrent Description


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