Difference between revisions of "TinyML"
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;Convolutional layers: contain filters that can be used to transform data. The values of these filters will be learned in the same way as the parameters in the Dense neuron you saw here. Thus, a network containing them can learn how to transform data effectively. This is especially useful in Computer Vision, which you’ll see later in this course. We’ll even use these convolutional layers that are typically used for vision models to do speech detection! Are you wondering how or why? Stay tuned! | ;Convolutional layers: contain filters that can be used to transform data. The values of these filters will be learned in the same way as the parameters in the Dense neuron you saw here. Thus, a network containing them can learn how to transform data effectively. This is especially useful in Computer Vision, which you’ll see later in this course. We’ll even use these convolutional layers that are typically used for vision models to do speech detection! Are you wondering how or why? Stay tuned! | ||
;Recurrent layers: learn about the relationships between pieces of data in a sequence. There are many types of recurrent layer, with a popular one called LSTM (Long, Short Term Memory), being particularly effective. Recurrent layers are useful for predicting sequence data (like the weather), or understanding text. | ;Recurrent layers: learn about the relationships between pieces of data in a sequence. There are many types of recurrent layer, with a popular one called LSTM (Long, Short Term Memory), being particularly effective. Recurrent layers are useful for predicting sequence data (like the weather), or understanding text. | ||
You’ll also encounter layer types that don’t learn parameters themselves, but which can affect the other layers. These include layers like '''dropouts''', which are used to reduce the density of connection between dense layers to make them more efficient, '''pooling''' which can be used to reduce the amount of data flowing through the network to remove unnecessary information, and '''lambda''' lambda layers that allow you to execute arbitrary code. | |||
https://www.hackster.io/news/easy-tinyml-on-esp32-and-arduino-a9dbc509f26c | https://www.hackster.io/news/easy-tinyml-on-esp32-and-arduino-a9dbc509f26c | ||
Revision as of 11:15, 11 December 2020
tinyML
- Dense layer
- Convolutional layers
- contain filters that can be used to transform data. The values of these filters will be learned in the same way as the parameters in the Dense neuron you saw here. Thus, a network containing them can learn how to transform data effectively. This is especially useful in Computer Vision, which you’ll see later in this course. We’ll even use these convolutional layers that are typically used for vision models to do speech detection! Are you wondering how or why? Stay tuned!
- Recurrent layers
- learn about the relationships between pieces of data in a sequence. There are many types of recurrent layer, with a popular one called LSTM (Long, Short Term Memory), being particularly effective. Recurrent layers are useful for predicting sequence data (like the weather), or understanding text.
You’ll also encounter layer types that don’t learn parameters themselves, but which can affect the other layers. These include layers like dropouts, which are used to reduce the density of connection between dense layers to make them more efficient, pooling which can be used to reduce the amount of data flowing through the network to remove unnecessary information, and lambda lambda layers that allow you to execute arbitrary code.
https://www.hackster.io/news/easy-tinyml-on-esp32-and-arduino-a9dbc509f26c
@Timothy_Bonner on plants light and water
So I've just had a replacement heart valve put in. I have a $35 Mi band that periodically reads my heart rate and other data. I have guidelines from my doctor that tell me to keep my heart rate within 20-30 beats-per-minute of my resting rate during exercise and I have an exercise program from the physical therapist on how much I can do during each week of my recovery. Maybe tinyML could combine the static data doc/pt guidelines with the 'edge' data of heart rate, distance, speed, hills, breathing rate, recovery from exercise rate, weight to produce a gentle reminder that maybe I could speed up a bit or slow down or take a rest or get off my butt in some way that optimizes my recovery.
A. Which one of these cases do you find most concerning? Which concerns you the least? B. What do you consider to be the relevant ethical challenges? C. What do you think the designers of this technology could have done differently? D. How can you apply learnings from these examples to your own job? Your personal life? E. Do you agree or disagree with what others have already posted in the forum? Why?
surveillance capitalism is pervasive
The Nest fiasco is one example. When we had guests over and we were chatting in the living room and Alexa said "I am sorry i didn't quite hear that" is another. But the damage to civil society by the algorithms of Facebook, YouTube and Twitter are incalculable, hidden behind the wilful obfuscation and disregard for privacy law by big tech. It is a major flaw of the internet that we depend on big tech to authenticate us giving them unbridled access to our data and its use by AI to mold the very way we think. We need to authenticate ourselves and keep control and ownership of our own data. Tim Berners Lee would agree. If our esteemed teacher uses OK Google as an example for the 20th time without touching on the implications I think my head will explode. Why do we have to worship at the alter of big tech. IOT is their next frontier. We need to watch out.
