Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
https://www.newworldai.com/cs230-deep-learning-stanford-engineering/
https://www.newworldai.com/cs230-deep-learning-stanford-engineering/
New World : Artificial Intelligence
CS230 Deep Learning Lectures | Stanford Engineering - New World : Artificial Intelligence
Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional…
Speed-up the Python code by thousands of times using Cython
https://towardsdatascience.com/boosting-python-scripts-with-cython-applied-on-raspberry-pi-5ea191292e68
https://towardsdatascience.com/boosting-python-scripts-with-cython-applied-on-raspberry-pi-5ea191292e68
Medium
Boosting Python Scripts With Cython (Applied on Raspberry Pi)
Speed-up the Python code by thousands times using Cython.
Efficient Transformers: A Survey
Tay et al.: https://arxiv.org/abs/2009.06732
#Transformer #DeepLearning #ArtificialIntelligence
Tay et al.: https://arxiv.org/abs/2009.06732
#Transformer #DeepLearning #ArtificialIntelligence
Nguyen et al., Super-Human Performance in Online Low-latency Recognition of Conversational Speech. arXiv:2010.03449 [cs.CV]. arxiv.org/abs/2010.03449
Fourier Neural Operator for Parametric Partial Differential Equations
https://arxiv.org/abs/2010.08895
https://arxiv.org/abs/2010.08895
Dive into Deep Learning with this machine learning course taught by industry veterans.
You'll learn about Random Forests, Gradient Descent, Recurrent Neural Networks, and other key coding concepts. All you need to get started with this course is some Python knowledge and a little high school math. And if you need to brush up on those, freeCodeCamp has you covered. (15 hour YouTube course):
#deeplearning
https://www.freecodecamp.org/news/learn-deep-learning-from-the-president-of-kaggle/
You'll learn about Random Forests, Gradient Descent, Recurrent Neural Networks, and other key coding concepts. All you need to get started with this course is some Python knowledge and a little high school math. And if you need to brush up on those, freeCodeCamp has you covered. (15 hour YouTube course):
#deeplearning
https://www.freecodecamp.org/news/learn-deep-learning-from-the-president-of-kaggle/
freeCodeCamp.org
Dive into Deep Learning with this free 15-hour YouTube Course
Deep Learning can help computers perform human-like tasks such as speech recognition and image classification. With Deep Learning – a form of Machine Learning (Artificial Intelligence) – computers can extract and transform data using multiple layers of neural…
Interested in Learning for Safety-Critical Control?
Check talk at the "Physics ∩ ML" Seminar
http://www.physicsmeetsml.org/
Title: Learning for Safety-Critical Control in Dynamical Systems
Abstract:
This talk describes ongoing research at Caltech on integrating learning into the design of safety-critical controllers for dynamical systems. To achieve control-theoretic guarantees while using powerful function classes such as neural networks, we must carefully integrate conventional control principles with learning into unified frameworks. I will present two paradigms: integration in dynamics modeling and in policy/controller design. Special emphasis on methods that both admit relevant safety guarantees and are practical to deploy.
Featuring work by Caltech students/postdocs/alumni:
Hoang Le (http://hoangle.info/)
Guanya Shi (http://gshi.me/)
Anqi Liu (https://anqiliu-ai.github.io/)
Richard Cheng (https://rcheng805.github.io/)
Jialin Song (https://jialin-song.github.
Check talk at the "Physics ∩ ML" Seminar
http://www.physicsmeetsml.org/
Title: Learning for Safety-Critical Control in Dynamical Systems
Abstract:
This talk describes ongoing research at Caltech on integrating learning into the design of safety-critical controllers for dynamical systems. To achieve control-theoretic guarantees while using powerful function classes such as neural networks, we must carefully integrate conventional control principles with learning into unified frameworks. I will present two paradigms: integration in dynamics modeling and in policy/controller design. Special emphasis on methods that both admit relevant safety guarantees and are practical to deploy.
Featuring work by Caltech students/postdocs/alumni:
Hoang Le (http://hoangle.info/)
Guanya Shi (http://gshi.me/)
Anqi Liu (https://anqiliu-ai.github.io/)
Richard Cheng (https://rcheng805.github.io/)
Jialin Song (https://jialin-song.github.
Microsoft Introduces Lobe: A Free Machine Learning Application That Allows You To Create AI Models Without Coding
https://www.marktechpost.com/2020/10/28/microsoft-introduces-lobe-a-free-machine-learning-application-that-allows-you-to-create-ai-models-without-coding/
GitHub: https://github.com/lobe
https://www.marktechpost.com/2020/10/28/microsoft-introduces-lobe-a-free-machine-learning-application-that-allows-you-to-create-ai-models-without-coding/
GitHub: https://github.com/lobe
MarkTechPost
Microsoft Introduces Lobe: A Free Machine Learning Application That Allows You To Create AI Models Without Coding
Microsoft has released Lobe, a free desktop application that lets Windows and Mac users create customized AI models without writing any code. Several customers are already using the app for tracking tourist activity around coral reefs, the company said. Lobe is…