🧠 Topic: Algorithms for Reinforcement Learning
💠 Link: https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf
💡 Tags: #reinforcement_learning #rl
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💠 Link: https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf
💡 Tags: #reinforcement_learning #rl
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🧠 Topic: Reinforcement Learning (Stanford)
💠 Link: https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u
💡 Tags: #reinforcement_learning #rl
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💠 Link: https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u
💡 Tags: #reinforcement_learning #rl
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YouTube
Stanford CS234: Reinforcement Learning | Winter 2019
This class will provide a solid introduction to the field of RL. Students will learn about the core challenges and approaches in the field, including general...
🧠 Topic: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
💠 Link: https://arxiv.org/abs/1703.03400
💡 Tags: #meta_learning #maml #paper #optimization_based
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💠 Link: https://arxiv.org/abs/1703.03400
💡 Tags: #meta_learning #maml #paper #optimization_based
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arXiv.org
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning...
🧠 Topic: On First-Order Meta-Learning Algorithms
💠 Link: https://arxiv.org/abs/1803.02999
💡 Tags: #meta_learning #reptile #paper #optimization_based
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💠 Link: https://arxiv.org/abs/1803.02999
💡 Tags: #meta_learning #reptile #paper #optimization_based
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arXiv.org
On First-Order Meta-Learning Algorithms
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously...
🧠 Topic: Meta-Learning with Memory-Augmented Neural Networks
💠 Link: http://proceedings.mlr.press/v48/santoro16.pdf
💡 Tags: #meta_learning #paper #model_based
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💠 Link: http://proceedings.mlr.press/v48/santoro16.pdf
💡 Tags: #meta_learning #paper #model_based
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🧠 Topic: META-LEARNING PROBABILISTIC INFERENCE FOR PREDICTION
💠 Link: https://arxiv.org/pdf/1805.09921.pdf
💡 Tags: #meta_learning #paper
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💠 Link: https://arxiv.org/pdf/1805.09921.pdf
💡 Tags: #meta_learning #paper
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🧠 Topic: 30 Essential Python Tips and Tricks for Programmers
💠 Link: https://www.techbeamers.com/essential-python-tips-tricks-programmers/
💡 Tags: #python
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💠 Link: https://www.techbeamers.com/essential-python-tips-tricks-programmers/
💡 Tags: #python
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TechBeamers
Python: 30 Programming Tips & Tricks
Welcome! If you’re looking for super cool Python tips and coding snippets to reuse, you’ve come to the right place. Discover the 30 hottest Python tips and tricks, perfect for coders at both basic and…
🧠 Topic: Episodic Meta-RL and the Brain
💠 Link: https://towardsdatascience.com/part-1-episodic-meta-rl-and-the-brain-909b9bccfc29
💡 Tags: #meta_learning #reinforcement_learning #neuroscience
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🧩 More: @Aitive
💠 Link: https://towardsdatascience.com/part-1-episodic-meta-rl-and-the-brain-909b9bccfc29
💡 Tags: #meta_learning #reinforcement_learning #neuroscience
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Medium
Part 1: Episodic Meta-RL and the Brain
Bridging the gap between machine learning and neuroscience
🧠 Topic: Meta-Learning With
Latent Embedding Optimization
💠 Link: https://arxiv.org/pdf/1807.05960
💡 Tags: #meta_learning #paper
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Latent Embedding Optimization
💠 Link: https://arxiv.org/pdf/1807.05960
💡 Tags: #meta_learning #paper
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🧠 Topic: I Thought I Was Mastering Python Until I Discovered These Tricks
💠 Link: https://towardsdatascience.com/i-thought-i-was-mastering-python-until-i-discovered-these-tricks-e40d9c71f4e2
💡 Tags: #python
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🧩 More: @Aitive
💠 Link: https://towardsdatascience.com/i-thought-i-was-mastering-python-until-i-discovered-these-tricks-e40d9c71f4e2
💡 Tags: #python
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Medium
I Thought I Was Mastering Python Until I Discovered These Tricks
Python best practices and tips that will make you code quickly and efficiently
🧠 Topic: Deep Learning - 2021 (Berkley)
💠 Link: https://www.youtube.com/playlist?app=desktop&list=PLki3HkfgNEsK1PNSxxPi1YuUKi2cvz5S5&ab_channel=ArtificialIntelligence
💡 Tags: #reinforcement_learning #rl #deep_learning #neural_network
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🧩 More: @Aitive
💠 Link: https://www.youtube.com/playlist?app=desktop&list=PLki3HkfgNEsK1PNSxxPi1YuUKi2cvz5S5&ab_channel=ArtificialIntelligence
💡 Tags: #reinforcement_learning #rl #deep_learning #neural_network
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🧠 Topic: Deep Reinforcement Learning - 2020 (Berkley)
💠 Link: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
💡 Tags: #reinforcement_learning #rl #deep_learning
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💠 Link: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
💡 Tags: #reinforcement_learning #rl #deep_learning
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🧠 Topic: Meta Learning Tutorial
💠 Link: https://www.youtube.com/playlist?list=PLuv1FSpHurUdx3GqtdVTAWiODMW3tPh6w
💡 Tags: #meta_learning #deep_learning
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🧩 More: @Aitive
💠 Link: https://www.youtube.com/playlist?list=PLuv1FSpHurUdx3GqtdVTAWiODMW3tPh6w
💡 Tags: #meta_learning #deep_learning
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YouTube
null - YouTube
Python_Tricks_A_Buffet_of_Awesome_Python_Features_by_Dan_Bader_z.pdf
1.2 MB
🧠 Topic: Python Tricks: A Buffet of Awesome Python Features
💠 Link: https://realpython.com/products/python-tricks-book/
💡 Tags: #python #book
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💠 Link: https://realpython.com/products/python-tricks-book/
💡 Tags: #python #book
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Fluent_Python_Clear,_Concise,_and_Effective_Programming_by_Luciano.pdf
16.9 MB
🧠 Topic: Fluent Python
💠 Link: https://www.oreilly.com/library/view/fluent-python/9781491946237/
💡 Tags: #python #book
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💠 Link: https://www.oreilly.com/library/view/fluent-python/9781491946237/
💡 Tags: #python #book
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🧠 Topic: Intermediate Python
💠 Link: https://media-exp3.licdn.com/dms/document/C561FAQECTCRv7iXnsA/feedshare-document-pdf-analyzed/0/1621334957270?e=1623697200&v=beta&t=9WtlNtrttH2Upw53G-mOEsDExFw6l1-v-QROQ2TL3qI
💡 Tags: #python #book
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💠 Link: https://media-exp3.licdn.com/dms/document/C561FAQECTCRv7iXnsA/feedshare-document-pdf-analyzed/0/1621334957270?e=1623697200&v=beta&t=9WtlNtrttH2Upw53G-mOEsDExFw6l1-v-QROQ2TL3qI
💡 Tags: #python #book
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🧠 Topic: Reinforcement learning in the brain
💠 Link: https://www.princeton.edu/~yael/Publications/Niv2009.pdf
💡 Tags: #reinforcement_learning #rl #brain #neuroscience
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💠 Link: https://www.princeton.edu/~yael/Publications/Niv2009.pdf
💡 Tags: #reinforcement_learning #rl #brain #neuroscience
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🧠 Topic: Towards an integration of deep learning and neuroscience
💠 Link: https://www.biorxiv.org/content/biorxiv/early/2016/06/13/058545.full.pdf
💡 Tags: #reinforcement_learning #rl #deep_learning #neural_network #neuroscience
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💠 Link: https://www.biorxiv.org/content/biorxiv/early/2016/06/13/058545.full.pdf
💡 Tags: #reinforcement_learning #rl #deep_learning #neural_network #neuroscience
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🧠 Topic: Complete python 3 tutorial notebooks
💠 Link: https://github.com/Pierian-Data/Complete-Python-3-Bootcamp
💡 Tags: #python
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💠 Link: https://github.com/Pierian-Data/Complete-Python-3-Bootcamp
💡 Tags: #python
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GitHub
GitHub - Pierian-Data/Complete-Python-3-Bootcamp: Course Files for Complete Python 3 Bootcamp Course on Udemy
Course Files for Complete Python 3 Bootcamp Course on Udemy - Pierian-Data/Complete-Python-3-Bootcamp
🧠 Topic: Python Numpy Tutorial (with Jupyter and Colab)
💠 Link: https://cs231n.github.io/python-numpy-tutorial/#:~:text=A%20numpy%20array%20is%20a,the%20array%20along%20each%20dimension.
💡 Tags: #python #numpy
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💠 Link: https://cs231n.github.io/python-numpy-tutorial/#:~:text=A%20numpy%20array%20is%20a,the%20array%20along%20each%20dimension.
💡 Tags: #python #numpy
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cs231n.github.io
Python Numpy Tutorial (with Jupyter and Colab)
Course materials and notes for Stanford class CS231n: Deep Learning for Computer Vision.
🧠 Topic: Temporal Convolutional Networks, The Next Revolution for Time-Series?
💠 Link: https://towardsdatascience.com/temporal-convolutional-networks-the-next-revolution-for-time-series-8990af826567
💡 Tags: #cnn
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💠 Link: https://towardsdatascience.com/temporal-convolutional-networks-the-next-revolution-for-time-series-8990af826567
💡 Tags: #cnn
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Medium
Temporal Convolutional Networks, The Next Revolution for Time-Series?
This post reviews the latest innovations that include the TCN in their solutions. We first present a case study of motion detection and…