Amazon Video

How to Make a Simple Touch Switch #shorts #zaferyildiz #short #electronics #viral

Exactly how to Make a Simple Touch Switch

#shorts
#zaferyildiz
#short
#electronics

Amazon Video

How to Make a Simple Touch Switch #shorts #zaferyildiz #short #electronics #viral

How to Make a Simple Touch Switch

#shorts
#zaferyildiz
#short
#electronics

Amazon Video

Simple project |MALAYALAM| #electronics

______________________________________________
easy circuit, electronic devices circuit, Radar
______________________________________________

For any type of doubt or queries:-.
Whatsapp at +91 807833270 1.
Electronics with dev.
_____________________________________________.
@doitinnovations @ElectronicsWithDev.
_____________________________________________.

#electronics #battery #electrical #m 4tech #science.
______________________________________________.

Amazon Video

Simple project |MALAYALAM| #electronics

______________________________________________
simple circuit, electronics circuit, Radar
______________________________________________

For any doubt or inquiries :-
Whatsapp at +91 8078332701
Electronics with dev
_____________________________________________
@doitinnovations @ElectronicsWithDev
_____________________________________________

#electronics #battery #electrical #m4tech #science
______________________________________________

Amazon Video

Future Electronics Sensors Booth at Embedded World 2022

Enter the Future Electronics sensors booth at the Embedded World convention 2022. Davide Osto, sensors expert at Future, guides us through a variety of devices and their applications. The booth displays technology ranging from ambient lighting sensors to CO2, temperature, humidity, and air pressure sensors. In addition, Davide presents reference evaluation kits and platforms as well as some astonishing camera-based sensors.

For more information, click here: https://www.futureelectronics.com/resources/events/embedded-world-2022

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About Future Electronics: https://www.futureelectronics.com

Follow us on LinkedIn: https://www.linkedin.com/company/future-electronics
Follow us on Twitter: https://twitter.com/futureelec

Amazon Video

Automatic Led Circuit Burning In The Dark #shorts #electronics

Automatic Led Circuit Burning In The Dark
#shorts
#electronic
#zaferyildiz
#short

Amazon Video

Automatic Led Circuit Burning In The Dark #shorts #electronics

Automatic Led Circuit Burning In The Dark
#shorts
#electronic
#zaferyildiz
#short

Amazon Video

Feature Selection for Embedded Machine Learning | Digi-Key Electronics

Function option is the processing of selecting which inputs (or features) are required when creating as well as educating an equipment discovering design. By finding which features are the most crucial, lesser functions can be dropped as inputs. This has the effect of conserving computational time, complexity, and memory.

This comes to be also extra vital in ingrained equipment learning, where resources are limited and a “attribute” may imply an entire sensing unit. Dropping an entire sensor to attain the very same results can suggest saving expenses, board space, as well as power!

You can read a composed tutorial showing these principles below: https://www.digikey.com/en/maker/projects/feature-selection-for-embedded-machine-learning/d9b3815901824489af5f46a023e25145

All code for this demonstration can be found below: https://github.com/ShawnHymel/perfect-toast-machine

Feature option is comparable to dimensionality reduction where we attempt to decrease the variety of worths utilized as inputs to a maker discovering model. Such strategies can reduce the computational intricacy of the design. Ideally, we wish to decrease the number of inputs while reducing any precision loss that could occur.

While dimensionality decrease generally needs a makeover of the information (hence sustaining some computational expenses), attribute choice allows us to determine which inputs we can drop altogether. This challenging component is figuring out which functions are unimportant.

Attribute selection is a broad (as well as still active) location of study that consists of a large number of techniques. In the video clip, we concentrate on two techniques: Pearson connection coefficient (PCC) for not being watched attribute option and Least Absolute Shrinkage as well as Selection Operator (LASSO) for monitored feature choice.

Relationship merely takes a look at the connection in between each set of input features. We can use scatter stories to visualize those partnerships or determine a “correlation toughness” number, such as the PCC. The PCC just provides a relative indication of direct correlation; it does not take non-linear partnerships into account.

On the various other hand, we can use supervised function selection techniques to educate a design as well as check out which of the inputs were essential in the decision-making process within the design. LASSO counts on adding an L1 regularization term to the very first layer of nodes and afterwards analyzing the resultant weights of that very first layer after training. Larger outright values show greater significance in the decision-making process, as well as weights closer to 0 means that the weights were reasonably useless.

We can educate the model once again using simply those attributes to make sure that we did not lose much precision as soon as we have actually figured out which features we desire to maintain. In the video, I apply these methods to the excellent toast device (https://youtu.be/meYZOXQo5mY) to eliminate two of the sensing units to attain the same results with less sensing units!

Product Links:
https://www.digikey.com/en/products/detail/seeed-technology-co-ltd/102991299/11689373

Related Videos:
AI Toaster That Makes Perfect Toast Using Smell: https://www.youtube.com/watch?v=meYZOXQo5mY
Making Use Of Sensor Fusion as well as Machine Learning to Create an AI Nose: https://www.youtube.com/watch?v=KyMC0LsLZms
Introductory to TinyML Part 1: https://www.youtube.com/watch?v=BzzqYNYOcWc
Introduction to TinyML Part 2: https://www.youtube.com/watch?v=dU01M61RW8s

Related Project Links:
Exactly how to Build an AI-powered Toaster: https://www.digikey.com/en/maker/projects/how-to-build-an-ai-powered-toaster/2268be5548e74ceca6830bf35f0f0f9e
How to Make an AI-powered Artificial Nose: https://www.digikey.com/en/maker/projects/how-to-make-an-ai-powered-artificialnose/3fcf88a89efa47a1b231c5ad2097716a

Relevant Articles:
What is Edge AI? Device Learning + IoT: https://www.digikey.com/en/maker/projects/what-is-edge-ai-machine-learning-iot/4f655838138941138aaad62c170827af
Edge-Based Machine Learning Application Development is Getting a Whole Lot Easier: https://www.digikey.com/en/blog/edge-based-machine-learning-application-development

Amazon Video

Feature Selection for Embedded Machine Learning | Digi-Key Electronics

Feature selection is the processing of choosing which inputs (or features) are necessary when creating and training a machine learning model. By discovering which features are the most important, less important features can be dropped as inputs. This has the effect of saving computational time, complexity, and memory.

However, this becomes even more important in embedded machine learning, where resources are scarce and a “feature” might mean an entire sensor. Dropping an entire sensor to achieve the same results can mean saving costs, board space, and power!

You can read a written tutorial demonstrating these concepts here: https://www.digikey.com/en/maker/projects/feature-selection-for-embedded-machine-learning/d9b3815901824489af5f46a023e25145

All code for this demonstration can be found here: https://github.com/ShawnHymel/perfect-toast-machine

Feature selection is similar to dimensionality reduction where we try to reduce the number of values used as inputs to a machine learning model. Such techniques can reduce the computational complexity of the model. Ideally, we want to reduce the number of inputs while minimizing any accuracy loss that might occur.

While dimensionality reduction usually requires a transformation of the data (thus incurring some computational costs), feature selection allows us to determine which inputs we can drop altogether. This difficult part is figuring out which features are unimportant.

Feature selection is a wide (and still active) area of research that includes a large number of techniques. In the video, we focus on two techniques: Pearson correlation coefficient (PCC) for unsupervised feature selection and Least Absolute Shrinkage and Selection Operator (LASSO) for supervised feature selection.

Correlation simply looks at the relationship between each pair of input features. We can use scatter plots to visualize those relationships or calculate a “correlation strength” number, such as the PCC. The PCC only provides a relative indication of linear correlation; it does not take non-linear relationships into account.

On the other hand, we can use supervised feature selection techniques to train a model and examine which of the inputs were most important in the decision-making process within the model. LASSO relies on adding an L1 regularization term to the first layer of nodes and then examining the resultant weights of that first layer after training. Larger absolute values indicate higher importance in the decision-making process, and weights closer to 0 means that the weights were relatively unimportant.

Once we have figured out which features we want to keep, we can train the model again using just those features to ensure that we did not lose much accuracy. In the video, I apply these techniques to the perfect toast machine (https://youtu.be/meYZOXQo5mY) to eliminate two of the sensors to achieve the same results with fewer sensors!

Product Links:
https://www.digikey.com/en/products/detail/seeed-technology-co-ltd/102991299/11689373

Related Videos:
AI Toaster That Makes Perfect Toast Using Smell: https://www.youtube.com/watch?v=meYZOXQo5mY
Using Sensor Fusion and Machine Learning to Create an AI Nose: https://www.youtube.com/watch?v=KyMC0LsLZms
Intro to TinyML Part 1: https://www.youtube.com/watch?v=BzzqYNYOcWc
Intro to TinyML Part 2: https://www.youtube.com/watch?v=dU01M61RW8s

Related Project Links:
How to Build an AI-powered Toaster: https://www.digikey.com/en/maker/projects/how-to-build-an-ai-powered-toaster/2268be5548e74ceca6830bf35f0f0f9e
How to Make an AI-powered Artificial Nose: https://www.digikey.com/en/maker/projects/how-to-make-an-ai-powered-artificialnose/3fcf88a89efa47a1b231c5ad2097716a

Related Articles:
What is Edge AI? Machine Learning + IoT: https://www.digikey.com/en/maker/projects/what-is-edge-ai-machine-learning-iot/4f655838138941138aaad62c170827af
Edge-Based Machine Learning Application Development is Getting a Whole Lot Easier: https://www.digikey.com/en/blog/edge-based-machine-learning-application-development

Amazon Video

AI Toaster That Makes Perfect Toast Using Smell | Digi-Key Electronics

The majority of kitchen counter toasters are basic home appliances that run a set of home heating coils on a timer. If you utilize different types of bread, thicknesses, or starting temperature levels without changing the timer, you could finish up with undercooked or worse, burned toast. I take on the evasive issue of making the best toast by using some ingrained equipment discovering hoax.

I customize (nay, hack) an inexpensive toaster oven to control the toasting process. By utilizing a collection of gas sensors, I can collect bountiful information on different smells launched during toasting. If we can scent when salute is burned, why can not a maker do the same?

I start by developing a sensor collection cage that installs a number of sensing units over a toaster. From there, I shed a hill of salute to accumulate enough gas data to train an equipment discovering design that can anticipate the moment remaining before the salute ends up being melted. This version is made use of to regulate the toasting process and quits the process time prior to the scorched stage is reached.

If you ‘d like to build your very own, a complete tutorial can be found below: https://www.digikey.com/en/maker/projects/how-to-build-an-ai-powered-toaster/2268be5548e74ceca6830bf35f0f0f9e

This job was inspired by Benjamin Cabe’s AI Nose job, which you can review here: https://blog.benjamin-cabe.com/

Previous project (AI nose): https://www.youtube.com/watch?v=KyMC0LsLZms

Introductory to TinyML Part 1: https://www.youtube.com/watch?v=BzzqYNYOcWc
Intro to TinyML Part 2: https://www.youtube.com/watch?v=dU01M61RW8s