JS + AI: (BYOM)

Build Your Own Model with TensorFlow.js!

WIFI:  connecttech

PW: webdev24

THE SCHEDULE

9:15 - LET'S TALK AI (45m)

10:00 - Teachable Machines

10:30 - BREAK

10:45 - DEMOS

11 - FIZZBUZZ

 

NOON - LUNCH

 

1 - Visual AI

2 - Basic LLMs

2:30 - BREAK

3 - Build a KNN

4 - Transfer Learning

 

4:30 - END

Popular AI

Since 2012

Popular AI

TEXT GENERATION

  1. ChatGPT: The Leading AI model
  2. Gemini: Google's competetor to ChatGPT
  3. Llama 3: Best AI open source model from Meta
  4. Claude 3: GPT competitor by Anthropic
  5. Perplexity: ChatGPT for research
  6. MM1: Apple's Large Language Model
  7. Titan: Amazon's LLM - Comes in Express & Premiere
  8. Grok: Elon Musk's OpenAI competitor
  9. CoPilot: Microsoft's Coding Assistant
  10. CodeWhisperer: Amazon's CoPilot Competetor
  11. Tabnine: Copilot competitor company
  12. Jasper: AI copilot for marketing
  13. Mistral AI: Open source text gen models

Popular AI

Generative Images

  1. Midjourney: Easy AI Image Generator
  2. Dall-E: OpenAI Image Generator
  3. Adobe Firefly: AI tool from Adobe
  4. Stable Diffusion: Best Image Generation
  5. Getty iStock: "Commercially Safe" generation
  6. Synthesia: AI text to video
  7. Sora: OpenAI text to video
  8. Runway: AI text-to-video
  9. Pika: AI Video Generator

Popular AI

OTHER POPULAR AI

  1. ElevenLabs: AI text-to-speech & voice clone
  2. Suno: AI music generator
  3. Voice.ai: AI voice changer
  4. AIMastering.com: AI Audio Mastering
  5. Lalal.AI: Remove instrument or voices from music
  6. Text to Speech and Speech to Text (Too many!)

WHAT IS MACHINE LEARINING?

  1. WE DON'T PROGRAM 
  2. WE CREATE TESTS
  3. MACHINES LEARN

THE TWO BIG BUCKETS

  1. Narrow AI
  2. General AI

EXAMPLES

Object Detection

EXAMPLES

NSFW JS

EXAMPLES

Tracking

EXAMPLES

Person Detection

EXAMPLES

Face Detection

EXAMPLES

Enjoy the Show

EXAMPLES

Facial Recognition - For Nic Cage

EXAMPLES

Face Mesh

EXAMPLES

Makeup Try on

EXAMPLES

Pose Detection

EXAMPLES

Counting Exercises

EXAMPLES

Scan Document

EXAMPLES

LANGUAGE OPTIONS

EXAMPLES

LANGUAGE OPTIONS

EXAMPLES

LANGUAGE OPTIONS

THE FUTURE

A combination of AI models working in concert to give excellent customer experience.

Anything you could do if you were given enough instruction and examples, is something an AI could do for you.

WHY NEURAL?

Neural Synapses Are

  • Densely connected
  • Have varying thickness in connections (weights)
  • Vary synapse size (bias)
  • Need a certain amount of electricity to fire, or activation voltage.

Example Problem

XOR operation

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

 

w=10

w=-10

w=10

w=-10

w=10

w=10

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

a = σ

w=10

w=-10

w=10

w=-10

w=10

w=10

0

1

b = -5

a = σ

0.9933

(0 * 10)
+ (1 * 10)
+ -5
= 5
σ(5) = 0.9933

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

a = σ

w=10

w=-10

w=10

w=-10

w=10

w=10

0

1

0.9933

(0 * -10)
+ (1 * -10)
+ 15
= 5
σ(5) = 0.9933

b = 15

 

a = σ

0.9933

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

a = σ

w=10

w=-10

w=10

w=-10

w=10

w=10

0

1

0.9933

(0.9933 * 10)
+ (0.9933 * 10)
+ -15
= 4.866
σ(4.866) ≈ 0.9924

0.9933

b = -15

a = σ

0.9924

DONE!

GPU Speed

Is this the same as code?

Matrix Math

Linear Algebra

\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix} \right)
\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix} \right)
\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 1 \\ 0 \\ 1 \end{bmatrix} \right)
\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix} \right)

Congratulations

You Understand Inference of a Basic Neural Network

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

 

w=10

w=-10

w=10

w=-10

w=10

w=10

1

2

3

4

5

6

7

8

9

Parameters

HOW?

MAGIC PARAMETER NUMBERS!?

HOW?

MAGIC PARAMETER NUMBERS!?

Math From Scratch?

NO!

Let's See Some AI Training

https://playground.tensorflow.org/

Let's Build a Model

https://teachablemachine.withgoogle.com/

WHY SHOULD YOU NOT USE THIS IN PRODUCTION?

Let's Code!

LUNCH?

VISUAL DATA

ROCK PAPER SCISSORS

Let's LLM

Understand LLMs deeper

COMPREHEND LLMS

https://infinite-red.slides.com/d/F7Pifi0/live

BUILD A KNN

TRANSFER LEARNING

https://aisortinghat.com/

Gant Laborde

Thanks!

GantLaborde.com

BYOM

By Gant Laborde

BYOM

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