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
10 machine learning YouTube videos.
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB

2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5

3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB

4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm

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.
\U0001f9f5\U0001f447
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.