OpenAI's ChatGPT AI (a neural network) functioning as a Linux system that I am able to navigate, read, write and execute scripts on.

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Watch this amazing 8K Ultra HD scenic wildlife film about African wildlife with music and real nature sounds. Discover the amazing world of animals wildlife of South Africa with us! Graceful lionesses, powerful falcons, leopards and rhinos, and many other representatives of the fauna of KwaZulu-Natal are ready to please you and distract you from problems! Get closer to amazing creatures that you can't stop watching! In this amazing scenic film about the wildlife of South Africa you will see:
African Buffalo, African Elephant, African Hoopoe, Amur Falcon, Arrow-Marked Babbler, Black Crake, Black Rhinoceros, Black-Backed Jackal, Blacksmith Lapwing, Blue Wildebeest, Brown-Hooded Kingfisher, Burchell's Zebra, Cape Starling, Cheetah, Common Warthog, Crowned Eagle, Emerald-Spotted Wood Dove, Golden Orb Web Spider, Hadada Ibis, Hamerkop, Impala, Laughing Dove, Leopard, Lilac-Breasted Roller, Lion, Long-Tailed Paradise Whydah, Marabou Stork, Nile Crocodile, Nile Monitor, Pied Kingfisher, Pin-Tailed Whydah, Praying Mantis, Red-Backed Shrike, Red-Billed Oxpecker, Secretarybird, Serval, Southern Giraffe, Spotted Hyena, Three-Banded Plover, Vervet Monkey, Water Thick-Knee, White Rhinoceros, White-Backed Vulture, White-Faced Whistling Duck and Yellow-Fronted Canary

Video from: KwaZulu Natal, South Africa
Video title: Wild Animals Of #KwaZulu Natal South Africa
Time of filming: Spring of 2021
Equipment used: RED cameras
Video type: scenic #wildlife film
Video resolution: #8K ULTRA HD
Producer: Roman Khomlyak, Pro Art Inc
Cinematographer: Robert Hofmeyr
Editor and colorist: Tetyana Nesterenko
Supervising editor: Anatolii Pylypenko

Here, on the territory of the famous KwaZulu Natal, the great five animals live, initially considered difficult to hunt and unpredictable. Lions, African Elephants, Leopards, Buffaloes and Rhinos are perfectly settled in the expanses of South African land. Along with them, this area are home to a huge number of other species of predators, herbivores, birds, insects and reptiles.

Spend time virtually in a beautiful place where animal energy is overflowing, and the sounds of wildlife are deafening. This 8K wildlife scenic film with music & nature sounds will give you unforgettable emotions from being close to the wild! Watch it on your 8K Ultra HD widescreen TV and enjoy every minute! Positive, relaxation and good mood are guaranteed!


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MB01K9NU77F34BR

Autoencoders are a family of neural nets that are well suited for unsupervised learning, a method for detecting inherent patterns in a data set. These nets can also be used to label the resulting patterns.

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Essentially, autoencoders reconstruct a data set and, in the process, figure out its inherent structure and extract its important features. An RBM is a type of autoencoder that we have previously discussed, but there are several others.

Autoencoders are typically shallow nets, the most common of which have one input layer, one hidden layer, and one output layer. Some nets, like the RBM, have only two layers instead of three. Input signals are encoded along the path to the hidden layer, and these same signals are decoded along the path to the output layer. Like the RBM, the autoencoder can be thought of as a 2-way translator.

Autoencoders are trained with backpropgation and a new concept known as loss. Loss measures the amount of information about the input that was lost through the encoding-decoding process. The lower the loss value, the stronger the net.

Some autoencoders have a very deep structure, with an equal number of layers for both encoding and decoding. A key application for deep autoencoders is dimensionality reduction. For example, these nets can transform a 256x256 pixel image into a representation with only 30 numbers. The image can then be reconstructed with the appropriate weights and bias; as an addition, some nets also add random noise at this stage in order to enhance the robustness of the discovered patterns. The reconstructed image wouldn’t be perfect, but the result would be a decent approximation depending on the strength of the net. The purpose of this compression is to the reduce the input size on a set of data before feeding it to a deep classifier. Smaller inputs lead to large computational speedups, so this preprocessing step is worth the effort.

Have you ever used an autoencoder to reduce the dimensionality of your data? Please comment and share your experiences.

Deep autoencoders are much more powerful than their predecessor, principal component analysis. In the video, you'll see the comparison of two letter codes associated with news stories of different topics. Among the two models, you’ll find the deep autoencoder to be far superior.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal