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Deep Learning Dictionary - Lightweight Crash Course

Deep Learning Course 1 of 6 - Level: Beginner

Activation Functions - Deep Learning Dictionary

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Activation Functions - Deep Learning Dictionary

In a neural network, an activation function applies a nonlinear transformation to the output of a layer.

Activation functions are biologically inspired by activity in our brains where different neurons fire (or are activated) by different stimuli.

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For any given node in a fully connected layer, recall that the node's value is calculated as the weighted sum of its inputs received from the previous layer. After taking this weighted sum, we pass the result to an activation function. This final result is passed as input to the following layer.

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The activation function does an operation on this sum to transform it to a value that is often times bounded between some lower and/or upper limit. A key point of this transformation is that it's non-linear.

An important feature of linear functions is that the composition of two linear functions is also a linear function. This means that, even in very deep neural networks, if we only had linear transformations of our data values during a forward pass, the learned mapping in our network from input to output would also be linear.

Typically, the types of mappings that we are aiming to learn with our deep neural networks are more complex than simple linear mappings. Having non-linear activation functions allows our neural networks to compute arbitrarily complex functions.

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There are many variations of activation functions, where each transform data differently and are used under different scenarios. Some of the more popular ones include:

  • ReLU
  • Sigmoid
  • Softmax

We'll elaborate on how each of these functions transform data in later lessons.

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What are activation functions within artificial neural networks? 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 Activation Functions 04:50 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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