<caption><image_11>The image is a diagram of a convolutional neural network. It consists of two layers, one labeled "Convolutional Neural Network" and the other labeled "Piping Layer". 

The first layer is a 3D network of neurons, which is a type of neural network that is connected to a network of nodes. The nodes are arranged in a grid-like pattern, with each node having a different color. The colors of the nodes vary, with some being blue, green, yellow, and red, while others are green, blue, and orange.

There is also a text box on the right side of the image that explains the concept of the neural network and how it can be used to communicate with other nodes in the network. The text box explains that each node has a 2.0 layer in depth, while the nodes have a depth of same weight. The image also shows a blue light shining on the top right corner, which could be a signal or a light source.</caption> images you know look at pixel by pixel and an NH you know maybe will score by grayscale and then we can convert it and this one showed you the even one is black and 25 is grade and then we have a little vector right now keep in mind these are pixels in the image right so if I have a 4 pixel of like 4 pixel if I've got an image of three key pictures were 32 kills it was RGB and what have the colors then we need 3000 slightly 3000 yeah I mean 32 byte here good pixels is like a thumbnail sketch right it's like an icon a Twitter pic to the for fix holes we're talking about you know hundred thousand ways we get $100 my input is effective hundred thousand elements and some people have like a hundred layers when you're starting with input of a hundred thousand vectors this is why when people were training you know networks it's a common thing to do is to get a bank of GPUs and you run the algorithm in CPUs even today people are building special deep learning is just more layers which means the conquered now there have been a bunch of different network configurations they use for neurons
{"source": "grass-yt-cc-by.1BknnZU_yOQ|shard_10013_19|create_multimodal_data.extract_yt_and_generate_transcripts.58|shard_10013|create_multimodal_data.generate_captions.58|shard_10013|create_multimodal_data.generate_captions.58", "params": {"id": "1BknnZU_yOQ", "channel_id": "UC_QKOkr6TT1e0-lQqbjc1-Q", "duration": 4174, "view_count": 47, "age_limit": 0, "chapters": [{"start_time": 0, "end_time": 193.0, "title": "<Untitled Chapter 1>"}, {"start_time": 193.0, "title": "Virus infection Simulation", "end_time": 544.0}, {"start_time": 544.0, "title": "Why We Sleep", "end_time": 1192.0}, {"start_time": 1192.0, "title": "Systematic way to change weights", "end_time": 1303.0}, {"start_time": 1303.0, "title": "Basic Algorithm", "end_time": 1377.0}, {"start_time": 1377.0, "title": "Basic Structure of Neuron", "end_time": 1573.0}, {"start_time": 1573.0, "title": "Varying Learning Rate", "end_time": 1677.0}, {"start_time": 1677.0, "title": "Varying Starting Point", "end_time": 1740.0}, {"start_time": 1740.0, "title": "Linear Neurons - Perceptrons", "end_time": 1766.0}, {"start_time": 1766.0, "title": "Types of Neurons/Activation Functions", "end_time": 1839.0}, {"start_time": 1839.0, "title": "Neural Networks Parameters", "end_time": 1874.0}, {"start_time": 1874.0, "title": "Layers", "end_time": 1941.0}, {"start_time": 1941.0, "title": "Forward Propagation", "end_time": 1972.0}, {"start_time": 1972.0, "title": "Back Propagation", "end_time": 2005.0}, {"start_time": 2005.0, "title": "Overfitting", "end_time": 2040.0}, {"start_time": 2040.0, "title": "Hyperparameters", "end_time": 2168.0}, {"start_time": 2168.0, "title": "Images & Scaling", "end_time": 2321.0}, {"start_time": 2321.0, "title": "Hello World of Deep Learning", "end_time": 2360.0}, {"start_time": 2360.0, "title": "Different Methods with Error Rate", "end_time": 2435.0}, {"start_time": 2435.0, "title": "Spark DeepLearning", "end_time": 2649.0}, {"start_time": 2649.0, "title": "AWS Free Tier", "end_time": 2698.0}, {"start_time": 2698.0, "title": "EC2 Pricing", "end_time": 2814.0}, {"start_time": 2814.0, "title": "Basic Outline", "end_time": 2856.0}, {"start_time": 2856.0, "title": "Simple Storage System - S3", "end_time": 2935.0}, {"start_time": 2935.0, "title": "S3 Creating a Bucket", "end_time": 3137.0}, {"start_time": 3137.0, "title": "Bucket Names", "end_time": 3163.0}, {"start_time": 3163.0, "title": "Access Control Lists (ACL)", "end_time": 3193.0}, {"start_time": 3193.0, "title": "S3 Data Consistency Model", "end_time": 3266.0}, {"start_time": 3266.0, "title": "CAP Theorem", "end_time": 3588.0}, {"start_time": 3588.0, "title": "Consistency over Latency", "end_time": 4174}], "like_count": 2, "channel_follower_count": 117, "license": "Creative Commons Attribution license (reuse allowed)", "uploader_id": "@rogerwhitneysdsucourses1456", "yt_tar_file": "/leonardo_work/EUHPC_E03_068/audio/fixed_download/grass-yt-cc-by//shard_10013/1BknnZU_yOQ.tar.gz"}}