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
Do you want to learn the maths for machine learning but don't know where to start?
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
Are you planning to learn Python for machine learning this year?
— Pratham Prasoon (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
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These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.