Awesome List Updates on Mar 21, 2017
8 awesome lists updated today.
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1. Awesome Elixir
Errors and Exception Handling
- OK (⭐599) - Elegant error handling with result monads, featuring a simple & powerful
withconstruct and a happy path pipe operator.
2. Awesome Crystal
Search
- hermes (⭐37) - Data Mapper pattern implementation for ElastiSearch
Task management
- sam (⭐95) - Another one Rake-like task manager with namespacing and arguments system
3. Awesome Jvm
Documentation
- Using JDK 9 Memory Order Modes - For expert programmers familiar with Java concurrency, but unfamiliar with the memory order modes available in JDK 9 provided by VarHandles.
4. Awesome Dev Fun
Elixir
- OOP (⭐281) - OOP in Elixir!
5. Awesome Cli Apps
Version Control / Git
- git-standup (⭐7.5k) - Recall what you did on the last working day.
6. Awesome Groovy
Testing
- HTTP Mock Server (⭐9) - HTTP Mock Server allows to mock HTTP request using groovy closures.
Staying up to date
- Groovy Calamari - Weekly curated publication about the Groovy Ecosystem
Blogs of core committer
7. Awesome Javascript
QA Tools / Runner
- JavaScript Standard Style (⭐29k) - Opinionated, no-configuration style guide, style checker, and formatter
8. Awesome Deep Learning Papers
Contents / Image: Segmentation / Object Detection
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [pdf]
Contents / Natural Language Processing / RNNs
- Neural Architectures for Named Entity Recognition (2016), G. Lample et al. [pdf]
Contents / More Papers from 2016
- Google's neural machine translation system: Bridging the gap between human and machine translation (2016), Y. Wu et al. [pdf]
Contents / New papers
- Learning to discover cross-domain relations with generative adversarial networks (2017), T. Kim et al. [pdf]
- Deep voice: Real-time neural text-to-speech (2017), S. Arik et al., [pdf]
- PixelNet: Representation of the pixels, by the pixels, and for the pixels (2017), A. Bansal et al. [pdf]
- Batch renormalization: Towards reducing minibatch dependence in batch-normalized models (2017), S. Ioffe. [pdf]
- Wasserstein GAN (2017), M. Arjovsky et al. [pdf]
- Understanding deep learning requires rethinking generalization (2017), C. Zhang et al. [pdf]
- Least squares generative adversarial networks (2016), X. Mao et al. [pdf]
Contents / Book / Survey / Review
- On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj. [pdf]
- Deep Reinforcement Learning: An Overview (2017), Y. Li, [pdf]
- Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig. [pdf]
Contents / Appendix: More than Top 100
- Improving distributional similarity with lessons learned from word embeddings, O. Levy et al. [[pdf]] (https://www.transacl.org/ojs/index.php/tacl/article/download/570/124)
- Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015), C. Dyer et al. [pdf]
- Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs (2015), M. Ballesteros et al. [pdf]
- Finding function in form: Compositional character models for open vocabulary word representation (2015), W. Ling et al. [pdf]
- A Fast and Accurate Dependency Parser using Neural Networks. Chen and Manning. [pdf]
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- Next: Mar 20, 2017