βοΈ AR-Net: A simple autoregressive NN for Time Series
πΉ blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
π paper: https://arxiv.org/abs/1911.03118
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πVia: @cedeeplearning
#timeseries #neuralnetworks #machinelearning #deeplearning
πΉ blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
π paper: https://arxiv.org/abs/1911.03118
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πVia: @cedeeplearning
#timeseries #neuralnetworks #machinelearning #deeplearning
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βͺοΈ Basics of Neural Network Programming
βοΈ by prof. Andrew Ng
πΉSource: Coursera
π Lecture 26 Activation Functions
Neural Networks and Deep Learning
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πVia: @cedeeplearning
πOther social media: https://linktr.ee/cedeeplearning
#DeepLearning #machinelearning #AI #coursera #free #python #math #activation_function #machinelearning #neuralnetworks
βοΈ by prof. Andrew Ng
πΉSource: Coursera
π Lecture 26 Activation Functions
Neural Networks and Deep Learning
ββββββββββ
πVia: @cedeeplearning
πOther social media: https://linktr.ee/cedeeplearning
#DeepLearning #machinelearning #AI #coursera #free #python #math #activation_function #machinelearning #neuralnetworks
βοΈ How You Should Read Research Papers According To Andrew Ng (Stanford Deep Learning Lectures)
Instructions on how to approach knowledge acquisition through published research papers by a recognized figure within the world of machine learning and education
π by Richmond Alake
link: https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3
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πVia: @cedeeplearning
#paper #research #stanford #deeplearning #andrew_ng
#neuralnetworks #math #machinelearning
Instructions on how to approach knowledge acquisition through published research papers by a recognized figure within the world of machine learning and education
π by Richmond Alake
link: https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3
βββββ
πVia: @cedeeplearning
#paper #research #stanford #deeplearning #andrew_ng
#neuralnetworks #math #machinelearning
Medium
How You Should Read Research Papers According To Andrew Ng (Stanford Deep Learning Lectures)
Instructions on how to approach knowledge acquisition through published research papers by a recognized figure.
βοΈ Neural Manifold Ordinary Differential Equations
π Article: https://arxiv.org/abs/2006.10254
πΉ Github: https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations
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πVia: @cedeeplearning
π Article: https://arxiv.org/abs/2006.10254
πΉ Github: https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations
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πVia: @cedeeplearning
Media is too big
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βͺοΈ Basics of Neural Network Programming
βοΈ by prof. Andrew Ng
πΉSource: Coursera
π Lecture 27 Why Non-linear Activation Functions
Neural Networks and Deep Learning
ββββββββββ
πVia: @cedeeplearning
πOther social media: https://linktr.ee/cedeeplearning
#DeepLearning #machinelearning #AI #coursera #free #python #math #activation_function #machinelearning #neuralnetworks
βοΈ by prof. Andrew Ng
πΉSource: Coursera
π Lecture 27 Why Non-linear Activation Functions
Neural Networks and Deep Learning
ββββββββββ
πVia: @cedeeplearning
πOther social media: https://linktr.ee/cedeeplearning
#DeepLearning #machinelearning #AI #coursera #free #python #math #activation_function #machinelearning #neuralnetworks
Building_Machine_Learning_Powered_Applications_Going_From_Idea_to.pdf
9.9 MB
π Building Machine Learning Powered Applications
Going from Idea to Product Emmanuel Ameisen
π@cedeeplearning
#book #ML #deeplearning #free #machinelearning
Going from Idea to Product Emmanuel Ameisen
π@cedeeplearning
#book #ML #deeplearning #free #machinelearning
βοΈ DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AI
https://youtu.be/7R52wiUgxZI
πvia: @cedeeplearning
#deepmind #ucl #deeplearning #lecture #AI #machinelearning
https://youtu.be/7R52wiUgxZI
πvia: @cedeeplearning
#deepmind #ucl #deeplearning #lecture #AI #machinelearning
YouTube
DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AI
In this lecture DeepMind Research Scientist and UCL Professor Thore Graepel explains DeepMind's machine learning based approach towards AI. He examples of how deep learning and reinforcement learning can be combined to build intelligent systems, includingβ¦