Tensorflow and deep learning - without a PhD by Martin Görner
Subscribe to Devoxx on YouTube @ https://bit.ly/devoxx-youtube
Like Devoxx on Facebook @ https://www.facebook.com/devoxxcom
Follow Devoxx on Twitter @ https://twitter.com/devoxx
Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required.
This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularisation techniques: dropout, learning rate decay, batch normalisation - recurrent neural networks - natural language analysis, word embeddings - transfer learning - image analysis - image generation - and many examples.
Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning.
[ULT-2698]
Видео Tensorflow and deep learning - without a PhD by Martin Görner канала Devoxx
Like Devoxx on Facebook @ https://www.facebook.com/devoxxcom
Follow Devoxx on Twitter @ https://twitter.com/devoxx
Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required.
This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularisation techniques: dropout, learning rate decay, batch normalisation - recurrent neural networks - natural language analysis, word embeddings - transfer learning - image analysis - image generation - and many examples.
Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning.
[ULT-2698]
Видео Tensorflow and deep learning - without a PhD by Martin Görner канала Devoxx
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![Deep Learning: A Crash Course](https://i.ytimg.com/vi/r0Ogt-q956I/default.jpg)
![Getting Started with TensorFlow and Deep Learning | SciPy 2018 Tutorial | Josh Gordon](https://i.ytimg.com/vi/tYYVSEHq-io/default.jpg)
![Tensorflow et l'apprentissage profond, sans les équations différentielles (Martin Görner)](https://i.ytimg.com/vi/BtAVBeLuigI/default.jpg)
![Neural Networks: Walkthrough by Katharine Beaumont](https://i.ytimg.com/vi/8Sd6KOm9vcM/default.jpg)
![TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)](https://i.ytimg.com/vi/t1A3NTttvBA/default.jpg)
![Restricted Boltzmann Machines - A friendly introduction](https://i.ytimg.com/vi/Fkw0_aAtwIw/default.jpg)
![Deep Learning Basics: Introduction and Overview](https://i.ytimg.com/vi/O5xeyoRL95U/default.jpg)
![TensorFlow, Deep Learning, and Modern Convolutional Neural Nets, Without a PhD (Cloud Next '18)](https://i.ytimg.com/vi/KC4201o83W0/default.jpg)
![TensorFlow: Machine Learning for Everyone](https://i.ytimg.com/vi/mWl45NkFBOc/default.jpg)
![TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)](https://i.ytimg.com/vi/u4alGiomYP4/default.jpg)
![Will a robot take my job? | The Age of A.I.](https://i.ytimg.com/vi/f2aocKWrPG8/default.jpg)
![Functional Programming Patterns with Java8 with Victor Rentea](https://i.ytimg.com/vi/F02LKnWJWF4/default.jpg)
![Deep Learning State of the Art (2020)](https://i.ytimg.com/vi/0VH1Lim8gL8/default.jpg)
![](https://i.ytimg.com/vi/jtI3X6a_Bbw/default.jpg)
![Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4](https://i.ytimg.com/vi/azOmzumh0vQ/default.jpg)
![Deconstructing REST Security by David Blevins](https://i.ytimg.com/vi/9CJ_BAeOmW0/default.jpg)
![The Role of AI and Machine Learning in Mechanical Engineering](https://i.ytimg.com/vi/pJvG6dr1sQQ/default.jpg)
![Deep Reinforcement Learning Tutorial for Python in 20 Minutes](https://i.ytimg.com/vi/cO5g5qLrLSo/default.jpg)
![Prof. Brian Cox - Machine Learning & Artificial Intelligence - Royal Society](https://i.ytimg.com/vi/YvEIEXE_NL0/default.jpg)
![The Deep End of Deep Learning | Hugo Larochelle | TEDxBoston](https://i.ytimg.com/vi/dz_jeuWx3j0/default.jpg)