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Deep Learning Dictionary - Lightweight Crash Course

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Neural Network Layers - Deep Learning Dictionary

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Neural Network Layers - Deep Learning Dictionary

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

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A general network consists of an input layer, which receives the input data, an output layer, which supplies the network predictions on the given input, and hidden layers, which process the data during a forward pass.

There are many types of layers that process data in different ways and are chosen and used for specific purposes.

For many computer vision tasks in deep learning, we make use of:

  • Fully connected layers (Also known as dense and linear layers)
  • Convolutional layers

When data is passed as input to a given layer, the nodes in that layer process the input and send the output to the following layer.

Different layers perform different transformations on their inputs, and some layers are better suited for some tasks than others. For example, convolutional layers are the most popular layer type used in networks that work with image data.

A fully connected layer is one that is fully connects each of its inputs to each of its outputs via weights. This can be shown as each node in a given layer being fully connected by weights to all nodes in the previous layer and all nodes in the following layer.

There are various types of networks, and actually, the network type is often categorized by its layer types and how these layers are organized within the network. For example, a convolutional neural network (CNN) is a network with convolutional layers.

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What are the layers 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 Layers 02:30 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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