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- Tensorflow Eager: a Multi-Stage, Python-Embedded DSL for Machine Learning
- RNN LSTM and Deep Learning Libraries
- An Empirical Study of the Dependency Networks of Deep Learning Libraries
- Knet: Beginning Deep Learning with 100 Lines of Julia
- Theano: a CPU and GPU Math Compiler in Python
- Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
- What We Can Do with Theano
- AI Watch Historical Evolution of Artificial Intelligence
- Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools
- Deep Learning with the Theano Python Library
- Fermilab Keras Workshop
- Fast and Scalable Machine Learning with Golang
- Tensorflow Tutorial Bharath Ramsundar Administrative Announcements
- Artificial Intelligence: Separating Hype from Reality
- Machine Learning and Deep Learning Frameworks and Libraries for Large-Scale Data Mining: a Survey
- The Deep Learning Compiler: a Comprehensive Survey
- Keras Tutorial – Python Deep Learning Library
- Creating Neural Networks in Python | Electronics360
- Pytorch: an Imperative Style, High-Performance Deep Learning Library
- High-Performance, Distributed Training of Large-Scale Deep Learning Recommendation Models
- Introduction of Theano (1) Hung-Yi Lee Introduction
- Getting Started with Artificial Intelligence Second Edition
- Getting Started with Theano
- Theano Documentation Release 0.10.0Beta1
- Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces Via Memory Networks
- Knet: Beginning Deep Learning with 100 Lines of Julia
- Tensorflow Tutorial
- The De-Democratization of AI: Deep Learning and the Compute Divide in Artificial
- Performance, Power, and Scalability Analysis of the Horovod Implementation of the CANDLE NT3 Benchmark on the Cray XC40 Theta
- Deep Neural Networks for Physics Analysis on Low-Level Whole- Detector Data at the LHC
- Deep Learning 101— a Hands-On Tutorial
- A Comparative Measurement Study of Deep Learning As a Service Framework
- Tensorflow Tutorial
- Performance, Power, and Scalability Analysis of the Horovod Implementation of the CANDLE NT3 Benchmark on the Cray XC40 Theta
- Software Libraries for Deep Learning
- What Do Programmers Discuss About Deep Learning Frameworks
- A Visualization Tool for Analyzing the Suitability of Software Libraries Via Their Code Repositories
- Automatic Differentiation in ML: Where We Are and Where We Should Be Going
- Avoiding GPU OOM for Dynamic Computational Graphs Training
- Analysis and Comparison of Distributed Training Techniques for Deep Neural Networks in a Dynamic Environment