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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.


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”— πŸ‘‰ For more in depth lessons, check out the Deep Learning Fundamentals course: πŸ”— πŸ•’πŸ¦Ž VIDEO SECTIONS πŸ¦ŽπŸ•’ 00:00 Welcome to DEEPLIZARD - Go to 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”— πŸ’ͺ CHECK OUT OUR FITNESS CHANNEL: πŸ”— 🧠 Use code DEEPLIZARD at checkout to receive 15% off your first Neurohacker order: πŸ”— ❀️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Mano Prime πŸ‘€ Follow deeplizard: Our vlog: Fitness: Facebook: Instagram: Twitter: Patreon: YouTube: πŸŽ“ Deep Learning with deeplizard: AI Art for Beginners - Deep Learning Dictionary - Deep Learning Fundamentals - Learn TensorFlow - Learn PyTorch - Natural Language Processing - Reinforcement Learning - Generative Adversarial Networks - Stable Diffusion Masterclass - πŸŽ“ Other Courses: DL Fundamentals Classic - Deep Learning Deployment - Data Science - Trading - πŸ›’ Check out products deeplizard recommends on Amazon: πŸ”— πŸ“• Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard's link: πŸ”— 🎡 deeplizard uses music by Kevin MacLeod πŸ”— ❀️ Please use the knowledge gained from deeplizard content for good, not evil.


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