Discover amazing wildlife of Amazon! In this scenic film you will see the most incredible wild animals and birds that call the jungle home! These are some of them: Jaguar, Giant River Otter, Red Howler Monkey, Capybara, Black-capped Squirrel Monkey, Sloth, Macaw, Harpy Eagle, Toucan, Tamarin and many more. Sit back and relax while enjoying this scenic relaxation film captured on 4K ULTRA HD footage along with relaxing music.

Don't forget to SUBSCRIBE for more Amazing Scenic VIDEOS!!!


My other Scenic Relaxation films:

Europe 4K - https://youtu.be/29Qhpz8pmhE

USA 4K - https://youtu.be/8RQS5_RIAz0

Norway 4K - https://youtu.be/gEfYyYAHLeA

Switzerland 4K - https://youtu.be/GVTO-kH3Nhg

Greece 4K - https://youtu.be/R8dRqZOauHw

Montenegro 4K - https://youtu.be/Iwcevh0pACw

Albania 4K - https://youtu.be/X6ohazu-qJQ

Hawaii 4K - https://youtu.be/Zf_E-Ri1CO0

Amazon 4K - https://youtu.be/-53mGJ4h5sQ

Iceland 4K - https://youtu.be/78AIWqdIP68

United Kingdom 4K - https://youtu.be/EfIKtWRrU3o

Portugal 4K - https://youtu.be/RwO0bLr9Bsc

Africa 4K - https://youtu.be/6yXuCf5tBlg

South America 4K - https://youtu.be/7W4FEOIpZtA

Wild Animals 4K - https://youtu.be/5kozt0uDa4c

Ocean Wildlife 4K - https://youtu.be/lBWZ9ls9-Oc

Australia 4K - https://youtu.be/DWnS-kyXmgE

Bora Bora - https://youtu.be/GgYZNXGOvXw

Indonesia - https://youtu.be/teLKdgOOlhs

#Amazon #RelaxationFilm #ScenicScenes


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Copyright information:
Footage used in the video is licensed.
Video footage copyright under standard license.

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

Links:
YouTube: https://www.youtube.com/ariseffai
Twitter: https://twitter.com/ari_seff
Homepage: https://www.ariseff.com

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