These networks are primary focus for compression tasks of data in Machine Learning.
Ever heard of Autoencoders?
The first time I saw a Neural Network with more output neurons than in the hidden layers, I couldn't figure how it would work?!
#DeepLearning #MachineLearning
Here's a little something about them: 🧵👇
These networks are primary focus for compression tasks of data in Machine Learning.
Later when someone needs, can just take that small representation and recreate the original, just like a zip file.📥
Our inputs and outputs are same and a simple euclidean distance can be used as a loss function for measuring the reconstruction.
Of course, we wouldn't expect a perfect reconstruction.
We are just trying to minimize the L here. All the backpropagation rules still hold.
▫️ Can learn non-linear transformations, with non-linear activation functions and multiple layers.
▫️ Doesn't have to learn only from dense layers, can learn from convolutional layers too, better for images, videos right?
▫️ Can make use of pre-trained layers from another model to apply transfer learning to enhance the encoder /decoder
🔸 Image Colouring
🔸 Feature Variation
🔸 Dimensionality Reduction
🔸 Denoising Image
🔸 Watermark Removal
More from Machine learning
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
Really enjoyed digging into recent innovations in the football analytics industry.
>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇♂️ https://t.co/9YOSrl8TdN
For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.
This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5
Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.
Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN
So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇♂️ https://t.co/9YOSrl8TdN
For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.
This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5
Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.
Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN
So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
With hard work and determination, anyone can learn to code.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
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