Lets look at how to increase efficiency using FP16.
This is a guide to make deep learning less messy and hopefully give you a way to use less tissues next time you code.
Today we will look at Super Resolution in Python.
Today we will talk about Federated Learning
Here we will see what happens when we “dont” take Batchnorm for granted.
How smart are neural networks? And can we break them and fool them into doing dumb things?
Hello dear reader, I share a quick rain check and announce my medium page :)
Today we will look at pruning and the different approaches followed.
Here we will talk about how to document your code using Documenter.jl and a few tips along the way.
Tiny post on datasets and a unified downloader for standard ones.
Here we will talk about Generative Networks and implement a simple version of DC GAN.
Deep Learning is dead. Hello Differentiable Programming. (Uh come on man)
Today we will talk about Compositional Pattern Producing Networks.
Here we will talk about VGG networks and how to implement VGG16 and VGG19.
Finally let us look at optimizers. Once that is done, we will be able to use Flux ML for a lot of things directly.
A simple Variational Auto Encoder using just what we made so far!!
Using the library functions which we defined till now to run a simple Neural Network.
(WIP Skip for now) NLP time!! Here we will look at an RNN from scratch.
Looking at backpropagation from scratch because somehow I have not done that yet.
Convert a video to low poly :) (See images below if you dont know what that is)
What makes Neural Networks tick mathematically.
We look at some image processing techniques and try to implement them from scratch.
What I found interesting from the book “Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD” by Jeremy Howard (Author), Sylvain Gugger (Author)
Here we will look at the pooling operation and its types.
Why CNNs are Correlation Neural Networks and an even faster Convolution operation.
Have you heard of fractal patterns? Here we will try to make some :) (look at the pictures at the end)
Continuing the action recognition project.
I try to reimplement Video recognition from Link and explain the code as I go along.
Here we will talk about spreading awareness about endangered species through AI powered art.
To implement a faster conv we need padding, so here we will try to explore what that means and try to implement it.
Here we will look at the various ways of implementing convolutions and benchmark them.
Image kernels are fun as filters, so let us just look at a few of them and maybe try something else?
Here we will talk about convolution and how to implement it from scratch.
How to deal with imposter syndrome and what causes it.
I want to talk about how to get these beautiful looking latex equations without any effort at all.
In this post we shall explore as many loss functions as I can find.
Notes for papers I read?
Notes from 100 Page ML Book
In this post we will try to implement SGD and read a bit about what it is.
Implementing batching for large data.
We explore the different initialization techniques that we have and look at papers to see which does better.
Let us start the fun with a simple model which will be extended to fit complex needs.
Implementing activation functions.
A roadmap of modules I want to implement. Mostly as a todo list and a help if anyone decides they want to pitch in. (LOL)
I want to convert my work as a script. Selectively.
Defining a function to split the dataset into train/test bits and oversample it as well.
The first thing we need is to be able to read data. To begin with, I am starting with the problem of image classification.
What I started with and how I set up this blog
An introduction to what I want to do and why.
I want to talk about adopting animals.