Getting Started with Embedded AI | Edge AI
- Descripción
- Currículum
- Reseñas
Nowadays, you may have heard of many keywords like Embedded AI /Embedded ML /Edge AI, the meaning behind them is the same, I.e. To make an AI algorithm or model run on embedded devices. Due to a massive gap between both technologies, techies don’t know where to start with it.
So we thought to share our engineer’s experience with you via this course. We have created an application to recognize the fault of a motor based on the vibration pattern. An Edge AI node developed to perform the analysis on the data captured from the accelerometer sensor to recognize the fault.
We have created detailed videos with animation to give our students an engaging experience while learning this stunning technology. We assure you will love this course after getting this hands-on experience.
The Motivation behind this course
One and half years back, It was surprising when techies heard of the embedded systems running standalone Deep learning model. We thought to design an application using this concept and share the same with you via this platform.
How to start the course?
There are two possible ways to start this course. We have divided this course into Conceptual Learning and Practical Learning. You can either jump directly to the Practical videos to keep the motivation to learn and later can go to fundamental concepts. Or you can start with the basic concepts first then can start building the application.
What you will get after enrolling in the course
1. You will get Conceptual + Practical clarity on Embedded AI
2. After this course you will be able to build similar kind of applications in Embedded AI
3. You will get all the Python scripts and C code(stm32) for Data capturing ,Data Labeling and Inference.
4.You will be able to know in depth working behind the neural networks
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Note – All the concepts are interlinked to each other may not possible to cover in one video. For more conceptual clarity keep on watching videos till the end. The doubt you will get in any video may get clear in another video. We tried to explain the same concept iteratively in different ways to make you familiar with the terminology.
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If you have any question or doubt, at any point, please message us immediately. We are eagerly ready to help you out and will try to solve your doubt or problem asap.
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13What is Supervised Learning?Video lesson
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14What is Unsupervised Learning?Video lesson
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15Artificial Neuron Vs Real NeuronVideo lesson
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16What is an Artificial Neural Network?Video lesson
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17What are layers and Forward propagation in NNVideo lesson
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18What is an Activation Function?Video lesson
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19What is Gradient and Gradient Descent?Video lesson
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20Optimization Algorithm and Loss functionVideo lesson
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21How a Neural Network Learns?Video lesson
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22The Concept of Loss functions in detailVideo lesson
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23The process of training and testing a NNVideo lesson
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24Why Overfitting occurs in NN and How to avoid it?Video lesson
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25Why Underfitting occurs in NN and How to avoid it?Video lesson
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26Hyperparameter of NN -> Learning RateVideo lesson
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27What is Batch and Batch size of a Training samples?Video lesson
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28Transfer Learning and Fine tuning Hyperparametrs in NNVideo lesson
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29What is Convolution?Video lesson
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30What is a Convolution Layer in NN?Video lesson
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31What is Max Pooling Layer?Video lesson
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32What is Dropout layer?Video lesson
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33One Hot Encoding of Output Classes or LabelsVideo lesson
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34What is Confusion Matrix?Video lesson
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35Difference between with or without normalization Confusion matrixVideo lesson
