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Steps to get started:

1. Install miniconda
https://docs.conda.io/en/latest/miniconda.html

2. Clone or download Stable Diffusion:
https://github.com/CompVis/stable-diffusion

3. Download weights for model:
https://huggingface.co/CompVis..../stable-diffusion-v-

4. Run text2img or img2img script in conda terminal as shown in video. Example:

python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms

Timestamps:
0:00 - Introduction
1:36 - GitHub Repo
5:23 - Clone Repository
6:10 - Create conda env
6:45 - Running script
8:10 - Arguments
11:30 - Example run
12:40 - Image to image example
15:40 - Understanding scale and steps
18:10 - Removing safety lol
18:45 - Ending

Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired by Google. They used neural networks to create algorithms that are able to play Atari games, learn them like a human would, eventually achieving superhuman performance.

Deep learning means that we use artificial neural network with multiple layers, making it even more powerful for more difficult tasks. These machine learning techniques proved to be useful for many tasks beyond image recognition: they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others.

In this episode, an intuitive explanation is given to show the inner workings of deep learning algorithms.

________________________

Original blog post by Christopher Olah (source of many images):
http://colah.github.io/posts/2....014-03-NN-Manifolds-

You can train your own deep neural networks on Andrej Karpathy's website:
http://cs.stanford.edu/people/....karpathy/convnetjs/d

Images used in this video:
Bunny by Tomi Tapio K (CC BY 2.0) - https://flic.kr/p/8EbcEk
Train by B4bees (CC BY 2.0) - https://flic.kr/p/6RzHe4
Train with bunny by Alyssa L. Miller (CC BY 2.0) - https://flic.kr/p/5WPeRN
The knot theory blackboard image was created by Clayton Shonkwiler (CC BY 2.0) https://flic.kr/p/64FYv
The tangled knot image was created by Mikael Hvidtfeldt Christensen (CC BY 2.0) https://flic.kr/p/beYG9D

The thumbnail image is a work of Duncan Hull (CC BY 2.0) - https://flic.kr/p/98qtJB

Subscribe if you would like to see more of these! - http://www.youtube.com/subscri....ption_center?add_use

Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu

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Web → https://cg.tuwien.ac.at/~zsolnai/

Checking out a fully custom water-cooled build with 4 A100 40GB cards. An insane powerhouse in a tiny form factor.

Comino Website: https://grando.ai/choose-a-gpu-machine-for-ai-deep-learning/?utm_source=youtube&utm_medium=video&utm_campaign=sentdex#grando-rm

Neural Networks from Scratch book: https://nnfs.io
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#comino #watercooling #deeplearning

Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more
https://www.youtube.com/channe....l/UCNU_lfiiWBdtULKOw In this video we will understand about the max pooling layer in CNN

Please do subscribe my other channel too
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Keras blog: https://blog.keras.io/building....-powerful-image-clas

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This short tutorial covers the basics of the Transformer, a neural network architecture designed for handling sequential data in machine learning.

Timestamps:
0:00 - Intro
1:18 - Motivation for developing the Transformer
2:44 - Input embeddings (start of encoder walk-through)
3:29 - Attention
6:29 - Multi-head attention
7:55 - Positional encodings
9:59 - Add & norm, feedforward, & stacking encoder layers
11:14 - Masked multi-head attention (start of decoder walk-through)
12:35 - Cross-attention
13:38 - Decoder output & prediction probabilities
14:46 - Complexity analysis
16:00 - Transformers as graph neural networks

Original Transformers paper:
Attention is All You Need - https://arxiv.org/abs/1706.03762

Other papers mentioned:
(GPT-3) Language Models are Few-Shot Learners - https://arxiv.org/abs/2005.14165
(DALL-E) Zero-Shot Text-to-Image Generation - https://arxiv.org/abs/2102.12092
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - https://arxiv.org/abs/1810.04805
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity - https://arxiv.org/abs/2101.03961
Finetuning Pretrained Transformers into RNNs - https://arxiv.org/abs/2103.13076
Efficient Transformers: A Survey - https://arxiv.org/abs/2009.06732
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth - https://arxiv.org/abs/2103.03404
Do Transformer Modifications Transfer Across Implementations and Applications? - https://arxiv.org/abs/2102.11972
Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies - https://ml.jku.at/publications/older/ch7.pdf
Transformers are Graph Neural Networks (blog post) - https://thegradient.pub/transf....ormers-are-graph-neu

Video style inspired by 3Blue1Brown

Music: Trinkets by Vincent Rubinetti

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