Tag: TensorFlow

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 3, Convolutional Residual Neural Networks

From: https://towardsdatascience.com/denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-3-convolutional-residual-6dbb36b28be So since I will be using a lot of image data, I will move on to Tensorflow to harness the power of GPU however, no worries, we will implement all of our back propagation. (Also compare the final results with auto differentiation). Now due to midterms I wasn’t able to do much, so … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 3, Convolutional Residual Neural Networks →

Using TensorFlow 2.0 to Compose Music

From: https://www.datacamp.com/community/tutorials/using-tensorflow-to-compose-music This tutorial was developed around TensorFlow 2.0 in Python, along with the high-level Keras API, which plays an enhanced role in TensorFlow 2.0. For those who would like to learn more about TensorFlow 2.0, see Introduction to TensorFlow in Python on DataCamp. For an exhaustive review of the deep learning for music literature, see Briot, Hadjerest, … Continue reading Using TensorFlow 2.0 to Compose Music →

Modeling “Unknown Unknowns” with TensorFlow Probability — Industrial AI, Part 3

From: https://medium.com/tensorflow/modeling-unknown-unknowns-with-tensorflow-probability-industrial-ai-part-3-52146cd0201a Posted by Venkatesh Rajagopalan, Director Data Science & Analytics; Mahadevan Balasubramaniam, Principal Data Scientist; and Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital We believe in a slightly modified version of George Box’s famous comment: “All models are wrong, some are useful” for a short period of time. Irrespective of how sophisticated a model … Continue reading Modeling “Unknown Unknowns” with TensorFlow Probability — Industrial AI, Part 3 →

Predicting Known Unknowns with TensorFlow Probability — Industrial AI, Part 2

From: https://medium.com/tensorflow/predicting-known-unknowns-with-tensorflow-probability-industrial-ai-part-2-2fbd3522ebda Posted by Venkatesh Rajagopalan, Director Data Science & Analytics and Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital In the first blog of this series, we presented our analytics philosophy of combining domain knowledge, probabilistic methods, traditional machine learning (ML) and deep learning techniques to solve some of the hardest problems in the industrial world. … Continue reading Predicting Known Unknowns with TensorFlow Probability — Industrial AI, Part 2 →

Industrial AI: BHGE’s Physics-based, Probabilistic Deep Learning Using TensorFlow Probability — Part 1

From: https://medium.com/tensorflow/industrial-ai-bhges-physics-based-probabilistic-deep-learning-using-tensorflow-probability-5f215c791863 By Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital Baker Hughes, a GE Company (BHGE), is the world’s leading fullstream oil and gas company with a mission to find better ways to deliver energy to the world. The BHGE Digital team develops enterprise grade, AI-driven, SaaS solutions to improve efficiency and reduce non-productive time … Continue reading Industrial AI: BHGE’s Physics-based, Probabilistic Deep Learning Using TensorFlow Probability — Part 1 →