Demo post

1. Demo post

$a = 2$, Hello how are you. Some more changes. Some more changes.\(a\alpha = 2\) Let us take this improvement to indicate how many times did deployed website shows that –layout errror– 8th April or 15th commit: 2 :3 (writing some more to see if length effects)

'''This is a random python code'''

from torch.optim import Optimizer 
import torch
import math


class AltGDA(Optimizer):
    def __init__(self,params,lr=1e-3):
        if lr < 0.0:
            raise ValueError
        defaults = dict(lr=lr)
        super(AltGDA,self).__init__(params,defaults)

    def step(self, dLoss, gLoss, closure=None):
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        dGroup = self.param_groups[0]
        gGroup = self.param_groups[1]

        dLoss.backward()
        for p in dGroup['params']:
            # boilerplate
            if p.grad is None:
                continue
            grad = p.grad.data
            if grad.is_sparse:
                raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
            # Algo
            p.data = p.data - dGroup['lr']*grad

        gLoss.backward()
        for p in gGroup['params']:
            # boilerplate
            if p.grad is None:
                continue
            grad = p.grad.data
            if grad.is_sparse:
                raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
            # Algorithm
            p.data = p.data - gGroup['lr']*grad

        return loss

\[\Large a \ne 2\]

2. Random 1

INSPIRATION DAY

This text is written in latex font. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.

Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.

Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

3. This is text in CMS

This text is written in CMS font without using html tagging. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence. Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

4. Random 2

Hey this is a prompt

5. Collapsing sections

Click here to see the implementation details

To Do list
1. Hello
2. How are you
Some code

            import numpy as np
            import pandas as pd

            data = pd.read_csv("filename")
            

### Hello **bold**

6. Chess Game

White	Black
1. e4	1. e5
2. Nf3	2. Nc6
3. Bc4	3. Bc5

7. H2

7.1 H3

This is some random text in h3

7.1.1 H4

7.2 H3

8. Ending

This is just a demo post to test out features of jekyll