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WATCH ON-DEMAND (No Ads/ Watermarks) @ https://watch.naturerelaxation.....com/videos/turtle-p | ABOUT: A sequel to the hit "Turtle Paradise" (https://youtu.be/VReCpkav3RY), this Part 2 film immerses viewers on a 2 hour long sea turtle filled underwater relaxation experience complete with brand new music by Relax Moods. The spectacular footage was filmed by underwater cinematographers Tilo & Yvonne Kühnast, and features an impeccable 8K resolution for maximum immersion thanks to the Sony Alpha 1. MORE INFO / LINKS:

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https://watch.naturerelaxation.....com/videos/australi | ABOUT: Prepare your senses for an ultra-relaxing and immersive Nature Relaxation™ experience from a beautiful Australian rainforest, filmed in ultra high resolution 8K. Paired with a soothing backdrop of stereo nature sounds and exclusive music by Relax Moods, "Australian Rainforest Relaxation" is sure to leave viewers feeling rejuvenated and full of inspiration thanks to its unique cinematography style which features a dynamic mix of stunning scenes in both real time and slow motion, and also employs the use of slowly moving shots to make for a more cinematic viewing experience. MORE INFO / CREDITS:

Filmed by Nature Relaxation™ Collaborator Elliot Manning
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Designed for therapeutic relaxation in patient care settings, such as hospitals, high stress workplaces, nursing homes, and for individuals at home that love Nature and are seeking enhanced vitality through their digital devices. Learn more about Nature Relaxation at https://www.NatureRelaxation.com/about

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#NatureRelaxation

High Quality HDR 12K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 16k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

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THESE 8K VIDEOS ARE FOR TV'S FOR DEMO AND TEST THE QUALITY OF ALL 8K TV'S.


🔥 I have done High color correction, Color changing, Raw Videos editing, HDR color Setting, merge files & 8K Export file etc.

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#12khdr #DolbyVision #60fps

If you are thinking about buying one... or two... GPUs for your deep learning computer, you must consider options like Ampere, GeForce, TITAN, and Quadro. These brands are rapidly evolving. In this video, I discuss this ever-changing landscape as of January 2021.

1:50 Cooling a GPU
2:40 Cooling 2 GPUs
3:10 NVLink
4:55 GPU Memory
7:00 NVIDIA GeForce 30 Series
8:26 Quadro
9:10 Multiple GPUs
14:14 More on NVLink
15:01 GPUs on Laptops
17:18 So what GPU would I get

Lenovo ThinkStation:
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~~~~~~~~~~~~~~~ BUY A GPU ~~~~~~~~~~~~~~~~~~~~

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#Python #Tensorflow #Keras

In this video we do the math for forward propagation in generalized notation!

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If deep neural networks are so powerful, why aren’t they used more often? The reason is that they are very difficult to train due to an issue known as the vanishing gradient.

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To train a neural network over a large set of labelled data, you must continuously compute the difference between the network’s predicted output and the actual output. This difference is called the cost, and the process for training a net is known as backpropagation, or backprop. During backprop, weights and biases are tweaked slightly until the lowest possible cost is achieved. An important aspect of this process is the gradient, which is a measure of how much the cost changes with respect to a change in a weight or bias value.

Backprop suffers from a fundamental problem known as the vanishing gradient. During training, the gradient decreases in value back through the net. Because higher gradient values lead to faster training, the layers closest to the input layer take the longest to train. Unfortunately, these initial layers are responsible for detecting the simple patterns in the data, while the later layers help to combine the simple patterns into complex patterns. Without properly detecting simple patterns, a deep net will not have the building blocks necessary to handle the complexity. This problem is the equivalent of to trying to build a house without the proper foundation.

Have you ever had this difficulty while using backpropagation? Please comment and let me know your thoughts.

So what causes the gradient to decay back through the net? Backprop, as the name suggests, requires the gradient to be calculated first at the output layer, then backwards across the net to the first hidden layer. Each time the gradient is calculated, the net must compute the product of all the previous gradients up to that point. Since all the gradients are fractions between 0 and 1 – and the product of fractions in this range results in a smaller fraction – the gradient continues to shrink.

For example, if the first two gradients are one fourth and one third, then the next gradient would be one fourth of one third, which is one twelfth. The following gradient would be one twelfth of one fourth, which is one forty-eighth, and so on. Since the layers near the input layer receive the smallest gradients, the net would take a very long time to train. As a subsequent result, the overall accuracy would suffer.

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