#diagram

neurons have biases and connections have weights

• Model: Artistic

• Size: 928 X 608 (0.56 MP)

• Used settings:

  • Prompt: each neuron has its own bias and each connection has its own weight
  • Using base image: Yes (Effect strength - 60%)
  • Base image resolution: Default
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

Patent drawings for a mind opener device

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: Patent drawings for a mind opener device
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

it is a well-known method called finite difference

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: in fact it is a well-known method called finite difference
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

neural net weights and biases

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: neural net weights and biases
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

Palaeohodites

• Model: Artistic

• Size: 864 X 672 (0.58 MP)

• Used settings:

  • Prompt: Palaeohodites
  • Using base image: Yes (Effect strength - 60%)
  • Base image resolution: Default
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

The AI in our heads is powered by vacuum tubes.

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: The AI in our heads is powered by vacuum tubes and valves.
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

cutting and pasting python

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: maybe it really is the cutting and pasting python where it really is. architecting how the parts that work on the math-like data join together to do actual things. dunno.
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

Electricity Field Diagram

• Model:

• Size: 1033 X 581 (0.60 MP)

• Level:

  • 0

• Used settings:

  • Enhance: Medium
  • Resolution: 0.6MP
  • Inception Depth: Deep
  • Layer: 19

knowledges of sausages

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: knowledges of sausages
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

true reasoning to help generalize better

• Model: Artistic

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: true reasoning to help generalize better
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

Tinygrad, developed by George Hotz (geohot)

• Model: Artistic 2

• Size: 896 X 704 (0.63 MP)

• Used settings:

  • Prompt: Tinygrad, developed by George Hotz (geohot), is a streamlined autograd library inspired by Micrograd. It aims to provide a simple and powerful solution for implementing deep learning models. Tinygrad is designed to be hardware-agnostic and supports both inference and training. It offers a similar API to PyTorch but with a simpler and more refined approach. Despite its simplicity, Tinygrad supports full forward and backward passes with autodiff, making it suitable for various machine learning projects .
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: landscape

mental map about Natural Language Processing

• Model: Stable

• Size: 768 X 768 (0.59 MP)

• Used settings:

  • Prompt: mental map about Natural Language Processing
  • Face enhance: No
  • Upscale & Enhance: 0
  • Aspect Ratio: square