sslides 0.0.27

Create awsome Solveit slides

sslides

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/rleyvasal/sslides.git

or from pypi

$ pip install sslides

Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on pypi.

How to use

1 Code cell with Import package - run after finish slides content

from sslides import sshow
sshow()

2 #| s to tell sslides all cells below are your slides 3 # header creaate the title slide 4 ## header creates regular slides - all content below heading is the slide content 5 run sslides

# from sslides import sshow
# sshow()

Introducing sslides

What is sslides

A tool for creating beautiful presentations from your solveit dialogs and research.

Share your learning by creating a slide deck from dialog.

Editing and navigation of slides without leaving the Dialog.

Create slides

1. Import sslides and function

from sslides import sshow
sshow()

2. #| s marks the begining of slides
3. # Creates a title slide
4. ## Creates a regular slides - all Markdown and code cells become content of in slide
5. Run sshow()

Navigate sslides

• Click on preview window to make it active
• Navigate with arrow keys
• Navigate with mouse - hover on bottom right corner to see controls and page number
• f key enters fullscreen mode
• esc key exits fullscreen

Content Suported

• Markdown
• Bullet lists
• Latex Math
• Attachments from screenshots
• Code - highlighted

All latex support

## Latex - Content too long, scroll available

### Scaled Dot-Product Attention

The attention mechanism from "Attention is All You Need":

$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

Where $Q$ is queries, $K$ is keys, $V$ is values, and $d_k$ is the dimension of the keys.

## Math Examples

Inline math: The quadratic formula is $x = \frac{-b \pm \sqrt{b^2-4ac}}{2a}$

Display math:
$$\int_a^b f(x)dx = F(b) - F(a)$$

Statistics:
$$\bar{x} = \frac{1}{n}\sum_{i=1}^n x_i$$

Calculus derivative:
$$\frac{d}{dx}(x^n) = nx^{n-1}$$

Matrix:
$$\begin{bmatrix} a & b \\ c & d \end{bmatrix}$$

Latex - Content too long, scroll available

Scaled Dot-Product Attention

The attention mechanism from “Attention is All You Need”:

$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

Where $Q$ is queries, $K$ is keys, $V$ is values, and $d_k$ is the dimension of the keys.

Math Examples

Inline math: The quadratic formula is $x = \frac{-b \pm \sqrt{b^2-4ac}}{2a}$

Display math: $$\int_a^b f(x)dx = F(b) - F(a)$$

Statistics: $$\bar{x} = \frac{1}{n}\sum_{i=1}^n x_i$$

Calculus derivative: $$\frac{d}{dx}(x^n) = nx^{n-1}$$

Matrix: $$\begin{bmatrix} a & b \ c & d \end{bmatrix}$$

Attachments

Plots

Activation Functions Comparisons

• Code hidden in Solveit = Code hidden in slides

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-3, 3, 1000)

# Activation functions
relu = np.maximum(0, x)
gelu = 0.5 * x * (1 + np.tanh(np.sqrt(2/np.pi) * (x + 0.044715 * x**3)))
swish = x / (1 + np.exp(-x))
leaky_relu = np.where(x > 0, x, 0.01 * x)

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, relu, label='ReLU', linewidth=2)
ax.plot(x, gelu, label='GELU', linewidth=2)
ax.plot(x, swish, label='Swish', linewidth=2)
ax.plot(x, leaky_relu, label='Leaky ReLU', linewidth=2, linestyle='--')

ax.axhline(0, color='black', linewidth=0.5, alpha=0.3)
ax.axvline(0, color='black', linewidth=0.5, alpha=0.3)
ax.grid(True, alpha=0.3)
ax.legend(fontsize=12)
ax.set_xlabel('Input', fontsize=12)
ax.set_ylabel('Output', fontsize=12)
ax.set_title('Activation Functions Comparison', fontsize=14, pad=15)
plt.tight_layout()
plt.show()