Ever wondered how the JVM debugger 'Evaluate' feature works in IDEs? Long ago I thought that the Java debug interface (JDI) actually allows you to specify the expression and get back its value.
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(1) Some haters of #Cardano are not only bag holders but also imperative developers.
If you are an imperative programmers you know that Plutus is not the most intuitive -> (https://t.co/m3fzq7rJYb)
It is, however, intuitive for people with IT financial background, e.g. banks
(2)
IELE + k framework will be a real game changer because there will be DSLs (Domain Specific Languages) in any programming language supported by K framework. The only issue is that we need to wait for all this
(3) Good news is that the moment we get IELE integrated into Cardano, we get some popular langs. To my knowledge we should get from day one: Solidity and Rust, maybe others as well?
List of langs: https://t.co/0uj1eBfrYj, some commits from many years ago..
@rv_inc ?
#Cardano
(a) Last but not least, marketing to people with Haskell, functional programming with experience and decision makers in banks is a tricky one, how do you market but not tell them you want to replace them. In the end one strategy is to pitch new markets, e.g. developing world
(b) As banks realize what is happening they maybe more inclined to join - not because they would like to but because they will have to - in such cases some development talent maybe re-routed to Plutus / Cardano / Algorand / Tezos
If you are an imperative programmers you know that Plutus is not the most intuitive -> (https://t.co/m3fzq7rJYb)
It is, however, intuitive for people with IT financial background, e.g. banks
(2)
IELE + k framework will be a real game changer because there will be DSLs (Domain Specific Languages) in any programming language supported by K framework. The only issue is that we need to wait for all this
(3) Good news is that the moment we get IELE integrated into Cardano, we get some popular langs. To my knowledge we should get from day one: Solidity and Rust, maybe others as well?
List of langs: https://t.co/0uj1eBfrYj, some commits from many years ago..
@rv_inc ?
#Cardano
(a) Last but not least, marketing to people with Haskell, functional programming with experience and decision makers in banks is a tricky one, how do you market but not tell them you want to replace them. In the end one strategy is to pitch new markets, e.g. developing world
(b) As banks realize what is happening they maybe more inclined to join - not because they would like to but because they will have to - in such cases some development talent maybe re-routed to Plutus / Cardano / Algorand / Tezos
THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
2/ For starters, let's look at what a neural network (NN for short) does.
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
3/ We show an image to our model.
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
4/ The picture goes into the layer at the bottom.
Each layer performs computation on the image, transforming it and passing it upwards.
5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
THREAD: Can you start learning cutting-edge deep learning without specialized hardware? \U0001f916
— Radek Osmulski (@radekosmulski) February 11, 2021
In this thread, we will train an advanced Computer Vision model on a challenging dataset. \U0001f415\U0001f408 Training completes in 25 minutes on my 3yrs old Ryzen 5 CPU.
Let me show you how...
2/ For starters, let's look at what a neural network (NN for short) does.
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
3/ We show an image to our model.
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
4/ The picture goes into the layer at the bottom.
Each layer performs computation on the image, transforming it and passing it upwards.
5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!