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Deep Learning Dictionary - Lightweight Crash Course

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Neural Network Nodes - Deep Learning Dictionary

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Neural Network Nodes/Neurons - Deep Learning Dictionary

An artificial neural network is made up of multiple processing units called nodes or neurons that are organized into layers. These layers are connected to each other via weights.

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The number of nodes in each layer depends partly on where the layer resides in the network, partly on the data that will eventually be passed to the layer, and partly on network architect's design choice for layer.

Layer Number of Nodes
Input layer Must have one node for each component/feature of the input data
Hidden layers Number of nodes is chosen (arbitrarily or empirically) by the network architect
Output layer Must have one node for each of the possible output classes

The nodes in the network are simply processing units for data that will eventually be passed to them.

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Each node has an output value that is the result of some operation that occurs between the data that is passed to it as input, along with the weights that connect the input to the node. The operation depends on the type of layer for which the node is part of.

The output from nodes in one layer are passed as input to nodes in the next layer.

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What are the nodes that make up an artificial neural network? πŸ‘‰ To gain early access to the full Deep Learning Dictionary course, register at: πŸ”— https://deeplizard.com/course/ddcpailzrd πŸ‘‰ For more in depth lessons, check out the Deep Learning Fundamentals course: πŸ”— https://deeplizard.com/course/dlcpailzrd πŸ•’πŸ¦Ž VIDEO SECTIONS πŸ¦ŽπŸ•’ 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:10 Neural Network Nodes 03:37 Collective Intelligence and the DEEPLIZARD HIVEMIND πŸ’₯🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎πŸ’₯ πŸ‘‹ Hey, we're Chris and Mandy, the creators of deeplizard! πŸ‘€ CHECK OUT OUR VLOG: πŸ”— https://youtube.com/deeplizardvlog πŸ’» DOWNLOAD ACCESS TO CODE FILES πŸ€– Available for members of the deeplizard hivemind: πŸ”— https://deeplizard.com/resources ❀️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Tammy BufferUnderrun Mano Prime πŸ‘€ Follow deeplizard: Our vlog: https://youtube.com/deeplizardvlog Facebook: https://facebook.com/deeplizard Instagram: https://instagram.com/deeplizard Twitter: https://twitter.com/deeplizard Patreon: https://patreon.com/deeplizard YouTube: https://youtube.com/deeplizard πŸŽ“ Deep Learning with deeplizard: Deep Learning Dictionary - https://deeplizard.com/course/ddcpailzrd Deep Learning Fundamentals - https://deeplizard.com/course/dlcpailzrd Learn TensorFlow - https://deeplizard.com/learn/video/RznKVRTFkBY Learn PyTorch - https://deeplizard.com/learn/video/v5cngxo4mIg Reinforcement Learning - https://deeplizard.com/learn/video/nyjbcRQ-uQ8 Generative Adversarial Networks - https://deeplizard.com/course/gacpailzrd πŸŽ“ Other Courses: Data Science - https://deeplizard.com/learn/video/d11chG7Z-xk Trading - https://deeplizard.com/learn/video/ZpfCK_uHL9Y πŸ›’ Check out products deeplizard recommends on Amazon: πŸ”— https://amazon.com/shop/deeplizard πŸ“• Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard's link: πŸ”— https://amzn.to/2yoqWRn 🎡 deeplizard uses music by Kevin MacLeod πŸ”— https://youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ πŸ”— http://incompetech.com/ ❀️ Please use the knowledge gained from deeplizard content for good, not evil.

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