爱可可老师24小时热门分享(2018.8.2)

No 1. 《Python与神经网络实战》
No 2. 《深度学习入门:基于Python的理论与实现》
No 3. 八条人生法则:1、学会适应 2、学会说不 3、耐心是种美德 4、努…
No 4. 《Python深度学习》
No 5. 【好久没用的文献管理工具Zotero,已经出独立客户端了,可配合浏览器插件使用,值得一试】
No 6. 【从fast.ai学到的十项技巧:用Fast.ai库、用多个学习速率、探索合适学习速率的方法、余弦退火、带重启动的随机梯度下降、人格化激活函数、NLP中用迁移学习超有效、深度学习有实力挑战结构化数据、building up sizes/dropout/TTA三板斧、创造力是关键】
No 7. 【伯克利分校课程:计算机程序结构与编译】
No 8. 【码农音乐创作利器Sonic Pi:用Ruby程序合成美妙音乐】
No 9. 【深度强化学习免费实例教程(Tensorflow)】
No 10. 继Twitter、arXiv之后,GitHub似乎也要变成“垃圾场”
No 11. 【论文写作指南之:关于独创性(如何对知识作出重大贡献)】
No 12. 【今日限免:Python数据结构与算法】
No 13. 【(基于IMDB)面向噪声控制的人脸识别数据集(170万人脸/近6万个人)】
No 14. 【AlphaGo Zero揭秘(SuperGo代码解析)】
No 15. 【基于Keras的AutoML机器学习自动化库】
No 16. 【Baidu发布(Paddle)深度Bi-GRU-CRF网络中文词法分析(LAC)工具:分词、词性标注、命名实体识别】
No 17. 来自NMT的报道:fast.ai联合创始人惨遭学生殴打,靠速度逃过一劫
No 18. 【两分钟论文解读之神经网络的对抗性重编】
No 19. 'Keras Visualization Toolkit with 3D gradCAM' by k…
No 20. 来来来,补上一堂历史课~ ​
No 21. 《Deep learning in agriculture: A survey》
No 22. 《t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data》
No 23. 【书稿:(R)数据可视化基础】
No 24. 需要用一生细细体会//八条人生法则:
1、学会适应
2、学会说不
3、耐心是…

No 25. 《Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder》
No 26. 【OpenCV视频数据处理工作流】
No 27. 海浪之美
No 28. 【衍射深度神经网络全光机器学习】
No 29. 【RNN标点还原(Wolfram)】
No 30. 这版本也不错 [笑cry]
No 31. 《MnasNet: Platform-Aware Neural Architecture Search for Mobile》
No 32. 新一代“破烂王”
No 33. 新章节:“Visualizing associations among two or more quantitative variables”
No 34. 【(杂志)计算机视觉新闻2018.8期】
No 35. 《爱可可老师24小时热门分享(2018.8.1)》
No 36. 【论文写作指南之学术观点的架构与表达】
No 37. “Accuracy”
No 38. 【AI时代的专利之战:尽管承诺开放,公司们还是在争相申请AI技术专利】
No 39. 《ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design》
No 40. 今天有朋友问起能处理中文的集成型NLP工具,简单汇总下:面向研究的StanfordNLP(Java…
No 41. 帮我们实现“超级英雄”
No 42. 《Count-Based Exploration with the Successor Representation》
No 43. 参数空间优化过程 src:http://weibo.com/1402400261/GrFzJv40B…
No 44. 欢迎参与!
No 45. 《A Survey of the Usages of Deep Learning in Natural Language Processing》
No 46. 原文出自今天这篇:http://t.cn/Re39A6r 下文是Google自动翻译的结果,看着有趣…
No 47. “NumPy 1.15.0 Released”
No 48. 《创业公司怎么招人? – 知乎》
No 49. 【百日机器学习编程计划】
No 50. 【(Keras)用卷积网络区分自然图像和生成图像】

爱可可老师24小时热门分享(2018.8.1)

No 1. 《深度学习入门:基于Python的理论与实现》
No 2. “Accuracy”
No 3. 《Python深度学习》
No 4. 【论文写作指南之学术观点的架构与表达】
No 5. 【贝叶斯学习教程】
No 6. 《我们看不懂一个数学证明仅仅是因为知识储备不够吗? – 知乎》
No 7. 这版本也不错 [笑cry]
No 8. 《ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design》
No 9. 【Baidu发布(Paddle)深度Bi-GRU-CRF网络中文词法分析(LAC)工具:分词、词性标注、命名实体识别】
No 10. 《创业公司怎么招人? – 知乎》
No 11. 今天有朋友问起能处理中文的集成型NLP工具,简单汇总下:面向研究的StanfordNLP(Java…
No 12. 八条人生法则:1、学会适应2、学会说不3、耐心是种美德4、努力总有回报5、快失败,早失败,…
No 13. 《TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing》
No 14. 【码农音乐创作利器Sonic Pi:用Ruby程序合成美妙音乐】
No 15. 【(R)美联储货币政策年度报告文本挖掘】
No 16. 【(Keras)用卷积网络区分自然图像和生成图像】
No 17. 【OpenCV视频数据处理工作流】
No 18. 【伯克利分校课程:计算机程序结构与编译】
No 19. 《A Survey of the Usages of Deep Learning in Natural Language Processing》
No 20. 【用AWS服务训练机器学习模型(顾客流失率预测)】
No 21. 【(基于IMDB)面向噪声控制的人脸识别数据集(170万人脸/近6万个人)】
No 22. 《Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes》
No 23. 《智能运维:从0搭建大规模分布式AIOps系统》
No 24. 《Deep Reinforcement Learning: Playing CartPole through Asynchronous Advantage Actor Critic (A3C) with tf.keras and eager execution》
No 25. 'ShuffleNet-V2 for both PyTorch and Caffe.' by Ste…
No 26. 来来来,补上一堂历史课~ ​
No 27. '中华新华字典数据库。包括歇后语,成语,汉字。提供新华字典API' by PWXCOO GitHub…
No 28. 【两分钟论文解读之向过往大师学习钢琴的DeepMind AI】
No 29. 'Keras Visualization Toolkit with 3D gradCAM' by k…
No 30. 【TensorFlow高级概率编程语言接口PyMC4】
No 31. 【书稿:(R)数据可视化基础】
No 32. 《当今中国应不应该降低数学基础教育的难度,同时拓展知识面? – 知乎》
No 33. 参数空间优化过程 src:http://weibo.com/1402400261/GrFzJv40B…
No 34. 帮我们实现“超级英雄”
No 35. 【OpenCV目标追踪】
No 36. 今日焦点:神经网络调试新方法——覆盖率引导模糊(CGF)调试方法…
No 37. 海浪之美
No 38. “NumPy 1.15.0 Released”
No 39. 《A Group-Theoretic Approach to Abstraction: Hierarchical, Interpretable, and Task-Free Clustering》
No 40. 666
No 41. 【深度强化学习免费实例教程(Tensorflow)】
No 42. 《爱可可老师24小时热门分享(2018.7.31)》
No 43. 《One-Shot Optimal Topology Generation through Theory-Driven Machine Learning》
No 44. 【百日机器学习编程计划】
No 45. 【Kaggle新赛:Airbus卫星图像船只检测】
No 46. 《Entropic Latent Variable Discovery》
No 47. 【今日限免:Python图形界面程序开发Cookbook】
No 48. 《2019 秋招的 AI 岗位竞争激烈吗? – 知乎》
No 49. 有人说学C才能成为真正的高手,没错,但不是每个人都需要成为高手;对于我们中的大多数,具备编程思维、能用算法解决问题就够了,能快速学习、简单、通用,让人尽快体会到“编程之美”
No 50. 新章节:“Visualizing associations among two or more quantitative variables”

爱可可老师24小时热门分享(2018.7.31)

No 1. 【百日机器学习编程计划】
No 2. 《2019 秋招的 AI 岗位竞争激烈吗? – 知乎》
No 3. 今天有朋友问起能处理中文的集成型NLP工具,简单汇总下:面向研究的StanfordNLP(Java…
No 4. 【OpenCV目标追踪】
No 5. 【贝叶斯学习教程】
No 6. 【Kaggle新赛:Airbus卫星图像船只检测】
No 7. 666
No 8. 【端到端语音处理工具集】
No 9. 【风格感知内容损失实时高清画风迁移】
No 10. 【构建学习结构和关系发现的模型】
No 11. 'ShuffleNet-V2 for both PyTorch and Caffe.' by Ste…
No 12. 《当今中国应不应该降低数学基础教育的难度,同时拓展知识面? – 知乎》
No 13. 《Research Proposal: Little Quick Fix》
No 14. 有人说学C才能成为真正的高手,没错,但不是每个人都需要成为高手;对于我们中的大多数,具备编程思维、能用算法解决问题就够了,能快速学习、简单、通用,让人尽快体会到“编程之美”
No 15. 【今日限免:Python图形界面程序开发Cookbook】
No 16. 《Python深度学习》
No 17. 《FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software》
No 18. 【(R)美联储货币政策年度报告文本挖掘】
No 19. 《From Adversarial Training to Generative Adversarial Networks》
No 20. 【用AWS服务训练机器学习模型(顾客流失率预测)】
No 21. 【进化计算百科(文献集)】
No 22. NBA投篮统计3D可视化,用AR实现一定更棒!
No 23. 《An Algorithm for Learning Shape and Appearance Models without Annotations》
No 24. 不论是入门难度、可移植性、开放资源丰富程度、社区成熟度,还是在AI领域的使用热门程度,Python应…
No 25. 【TensorFlow高级概率编程语言接口PyMC4】
No 26. 《Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image》
No 27. 《Progressive Neural Architecture Search》
No 28. 【计算机科学的道德准则:杜绝潜在的负面社会影响】
No 29. 【两分钟论文解读之向过往大师学习钢琴的DeepMind AI】
No 30. 《Auto-Encoding Variational Neural Machine Translation》
No 31. 《Symbolic Execution for Deep Neural Networks》
No 32. 重要的是,不要固步自封 – 别怕犯错,别怕看上去有多蠢,别在意那些取笑你、想羞辱你的人。探索、实践、…
No 33. 【手工操作学习】
No 34. '中华新华字典数据库。包括歇后语,成语,汉字。提供新华字典API' by PWXCOO GitHub…
No 35. 【深度神经网络与三维二元数独谜题(TensorFlow)】
No 36. PyTorch Implementation by Fangchang Ma GitHub:http…
No 37. 很重要、但未被充分认识的一点:作为研究人员,请确保经济激励与公共利益、科学进步保持一致。 特别是,资…
No 38. 《揭秘爱可可:“寂寞呆子”成长记》
No 39. 【在ACLU对名为“Rekognition”的面部识别工具的测试中,该软件错误地对28名国会议员进行了匹配,将他们识别为因犯罪而被捕的其他人】
No 40. Atari游戏的“人类表现”
No 41. 【PyTorch深度学习迷你教程】
No 42. Reddit:
No 43. PyTorch implementation of PNASNet-5 on ImageNet by…
No 44. 《Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks》
No 45. 《爱可可老师24小时热门分享(2018.7.30)》
No 46. 《智能运维:从0搭建大规模分布式AIOps系统》
No 47. 《Selective Clustering Annotated using Modes of Projections》
No 48. 《Practical Guide to Bare Metal C++》
No 49. “Best Comment”
No 50. 【把命令行操作过程录制成gif】

Artificial Intelligence News — Newsletter on Deep Learning & AI – Artificial Intelligence News #81

IN THE NEWS

  • Microsoft asks the U.S. Congress to regulate face recognition. More
  • A report by the Australian Strategic Policy Institute urges better oversight of international partnerships on AI, to ensure that collaborations are not being exploited for military uses. More
  • Facebook hires head of chip development from Google. More

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IN THE NEWS



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Deep Learning Weekly – 🤖 – Issue #87: AI Principles, 12TB RAM, AI Bubble, Tiny Devices, Software Stack 2.0, One-Shot Object Detection, Create ML and more… | Revue

 

Hey Folks! This week, we learn about Google’s AI principles, Microsoft announced machines with huge a
 
June 14 · Issue #87 · View online
Deep Learning Weekly

Hey Folks!
This week, we learn about Google’s AI principles, Microsoft announced machines with huge amounts of memory, first thoughts about an AI bubble begin to surface and we take a look at machine learning on tiny devices.
Andrej Karpathy shares some interesting views on the software stack 2.0 at Tesla, we learn everything about one-shot object detection and AutoAugment from Google, take a look at Apples new Create ML tool and get to know a machine learning toolchain built for artists.
As we just crossed 8000 subscribers, we would like to thank you once again for all of your support. As always, if you want to help us grow this great community of deep learning enthusiasts, simply share this issue with friends and colleagues.
See you next week!


Industry

AI at Google: our principles


Microsoft Azure will soon offer machines with up to 12 TB of memory


When the bubble bursts…


Why the Future of Machine Learning is Tiny

Why This Startup Created A Deep Learning Chip For Autonomous Vehicles

Sponsored Link

Mention DLWEEKLY for $400 USD discount on any Lambda Quad!
Learning

Building the Software 2.0 Stack by Andrej Karpathy from Tesla


One-shot object detection


Improving Deep Learning Performance with AutoAugment

Libraries & Code

Training a Text Classifier with Create ML and the Natural Language Framework

LSTM · ml5js

Papers & Publications

Improving Language Understanding with Unsupervised Learning

Why do deep convolutional networks generalize so poorly to small image transformations?

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Deep Learning Weekly – 🤖 – Issue #88: OpenAI Five, Twitter Workflows, Speech Synthesis, Inverse RL, DensePose, Confusing TensorFlow and more… | Revue

 

Hey and welcome to another week in deep learning! This week, we obviously start off with OpenAIs move
 
June 27 · Issue #88 · View online
Deep Learning Weekly

Hey and welcome to another week in deep learning!
This week, we obviously start off with OpenAIs move into Dota 2 gaming, look at ML Workflows at Twitter, learn about reduced GPU pricing on GCP and explore speech synthesis services as well as depth inference for photo editing. We learn about inverse reinforcement learning, object detection, and photo caption. Afterward, we get a great TensorFlow concepts explanation from a Google Brain resident, get to know Facebooks DensePose, a new portal linking papers and code, and the best paper of CVPR2018.
As always, we hope you’ll enjoy reading as much as we did and would appreciate you sharing this newsletter with friends and colleagues.
You may have noticed, that we’re currently running on a bi-weekly schedule due to various reasons, but hope to bring the newsletter back to the normal pace soon. To make up for the delays, we just added a few more links to this weeks issue.
Happy reading and hacking!


Industry

OpenAI Five


Productionizing ML with Workflows at Twitter


Introducing improved pricing for Preemptible GPUs


Speech Synthesis as a Service


From 2D to 3D Photo Editing

Learning

Learning from humans: what is inverse reinforcement learning?


Understanding Deep Learning for Object Detection


How to Develop a Deep Learning Photo Caption Generator from Scratch

Libraries & Code
Tensorflow: The Confusing Parts


Introducing Apex: PyTorch Extension with Tools to Realize the Power of Tensor Cores


Facebook open sources DensePose


NLP-progress: Repository to track the progress in Natural Language Processing (NLP)

Papers & Publications
Papers with Code: The latest in machine learning

Taskonomy: Disentangling Task Transfer Learning

On Calibration of Modern Neural Networks

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Deep Learning Weekly – Issue #90 – 🤖- Troubling Trends in Machine Learning Research, Design Patterns for Production NLP System & Improving Connectomics | Revue

 

Howdy folks and welcome to another week in deep learning! This week we take a look at the ominous use
 
July 19 · Issue #90 · View online
Deep Learning Weekly

Howdy folks and welcome to another week in deep learning!
This week we take a look at the ominous uses of AI by the Chinese government, some troubling trends in machine learning research, we learn about feature wise transforms and design patterns for production NLP systems
A fun little bit of trivia, the whimsical Not Hotdog App known from the HBO show Silicon Valley was built by fast.ai student and is now nominated for an Emmy.
Happy reading and hacking!


Industry

An Overview of National AI Strategies – Politics + AI


Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras


Apple’s New AI Chief Takes on Oversight of Siri

Facebook AI Research Expands With New Academic Collaborations

Troubling Trends in Machine Learning Scholarship

Sponsored Link

Deep Learning Weekly has secured a £200 discount off registration, quote DLW200!
Learning

Design Patterns for Production NLP Systems


Feature-wise Transformations


Using Deep Learning to Automatically Rank Millions of Hotel Images

Libraries & Code

Switchable-Normalization


A Project Based Introduction to TensorFlow.js – Knowledge-Exploration Systems

Papers & Publications
Google AI Blog: Improving Connectomics by an Order of Magnitude


Glow: Better Reversible Generative Models


Proceedings of Machine Learning Research

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

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Powered by Revue
 

 

爱可可老师24小时热门分享(2018.7.30)

No 1. 《Python深度学习》
No 2. 《如何从一个空有上进心的人,变成行动上的巨人? – 知乎》
No 3. '中华新华字典数据库。包括歇后语,成语,汉字。提供新华字典API' by PWXCOO GitHub…
No 4. 重要的是,不要固步自封 – 别怕犯错,别怕看上去有多蠢,别在意那些取笑你、想羞辱你的人。探索、实践、…
No 5. 【PyTorch深度学习迷你教程】
No 6. 【把命令行操作过程录制成gif】
No 7. 【Keras实现的Image OutPainting:用GAN绘制“画框外”的世界】
No 8. 所谓神似…… ​
No 9. 《是否真的存在天才? – 知乎》
No 10. 【对大量pdf文档模糊搜索的命令行工具】
No 11. 中国留学生回国率:1987年:5% 2007年:30% 2017年:79%src:http:…
No 12. 【今日限免:Python软件架构与设计】
No 13. “Best Comment”
No 14. 《Research Question: Little Quick Fix》
No 15. 【新书:TensorFlow预测分析】
No 16. 《Practical Guide to Bare Metal C++》
No 17. 《Bandit Algorithms》
No 18. 《Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks》
No 19. 【基于TensorFlow.js的张量计算(可微线代)试炼场(Web)】
No 20. 牛仔裤加工已经这么先进了? http://t.cn/Re6MLDF ​…
No 21. 《智能运维:从0搭建大规模分布式AIOps系统》
No 22. 无始无终(用单面镜、LED、钢铁制成) by Anthony James src:
No 23. 【用绵羊来解释熵——从融化的冰块到时间之谜】
No 24. 机器学习的每一波“浪潮”
No 25. 回顾:追踪NLP最新技术进展的两个好地方:NLP-progress http://t.cn/Rgk6…
No 26. 《The Hitchhiker's Guide to Tensorflow – Introduction to Machine Learning and Tensorflow – YouTube》
No 27. 《Learning the effect of latent variables in Gaussian Graphical models with unobserved variables》
No 28. 《Distributed Second-order Convex Optimization》
No 29. 《Deep Sequential Multi-camera Feature Fusion for Person Re-identification》
No 30. 该来的总会来:AR甲油试妆App——最流行的,永远不必是技术上最难的 ​​​…
No 31. 《General Value Function Networks》
No 32. 《Recurrent Capsule Network for Relations Extraction: A Practical Application to the Severity Classification of Coronary Artery Disease》
No 33. 《Decision Variance in Online Learning》
No 34. 《Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data》
No 35. 四大生成模型优缺点比较:Autoregressive, VAE, Normalized, and G…
No 36. 【自然语言处理好好玩】
No 37. 《爱可可老师24小时热门分享(2018.7.29)》
No 38. 《Iterative Amortized Inference》
No 39. 【PyTorch指南:使用、思考、技巧与陷阱】
No 40. “神似”
No 41. 【CT扫描数据集】
No 42. 【文本分类数据准备/模型选择流程图】
No 43. 《Multimodal Social Media Analysis for Gang Violence Prevention》
No 44. 欢迎参与!
No 45. 【超智AI的谬误与事实】
No 46. 物理老师伯恩斯先生制作的双黑洞发射#引力波#可视化 http://t.cn/RdPNH3e ​…
No 47. 【Kaggle比赛实战教程(Pandas, Matplotlib, XGBoost/Colab)】
No 48. 替朋友们专门查了下,此App叫“WANNA NAILS”
No 49. 【Kaggle出品的小型数据科学系列课程】
No 50. 《Automatically Designing CNN Architectures for Medical Image Segmentation》

爱可可老师24小时热门分享(2018.7.29)

No 1. 《Python深度学习》
No 2. 回顾:追踪NLP最新技术进展的两个好地方:NLP-progress http://t.cn/Rgk6…
No 3. 机器学习的每一波“浪潮”
No 4. 无始无终(用单面镜、LED、钢铁制成) by Anthony James src:
No 5. [笑cry] ​
No 6. 四大生成模型优缺点比较:Autoregressive, VAE, Normalized, and G…
No 7. 该来的总会来:AR甲油试妆App——最流行的,永远不必是技术上最难的 ​​​…
No 8. 中国留学生回国率:1987年:5% 2007年:30% 2017年:79%src:http:…
No 9. 【CT扫描数据集】
No 10. 牛仔裤加工已经这么先进了? http://t.cn/Re6MLDF ​…
No 11. “Best Comment”
No 12. 学术圈最棒的一点是自己可以当自己的老板,最糟糕的一点是我老板是个笨蛋。
No 13. 不论是入门难度、可移植性、开放资源丰富程度、社区成熟度,还是在AI领域的使用热门程度,Python应…
No 14. 《智能运维:从0搭建大规模分布式AIOps系统》
No 15. “神似”
No 16. 《Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches》
No 17. “The Markov-chain Monte Carlo Interactive Gallery”
No 18. 【Kaggle比赛实战教程(Pandas, Matplotlib, XGBoost/Colab)】
No 19. 步进视错觉 ​
No 20. 【在自字符界面绘制图表的命令行工具】
No 21. 《The Hitchhiker's Guide to Tensorflow – Introduction to Machine Learning and Tensorflow – YouTube》
No 22. 记住:科学的目的是产生知识,而非公关(PR)。应该根据是否能回答某些开放的科学问题,来选择研究项目—…
No 23. 替朋友们专门查了下,此App叫“WANNA NAILS”
No 24. 《Automatically Designing CNN Architectures for Medical Image Segmentation》
No 25. 精彩创意:360° Book http://t.cn/Rex1YDA ​
No 26. 【PyTorch指南:使用、思考、技巧与陷阱】
No 27. 【用绵羊来解释熵——从融化的冰块到时间之谜】
No 28. 《Meta-Learning Priors for Efficient Online Bayesian Regression》
No 29. 《Deep Supervision with Intermediate Concepts》
No 30. 起跑线~ ​
No 31. 《Hierarchical Classification using Binary Data》
No 32. 《2018自然语言处理研究报告》
No 33. 《The “TerpreT problem” and the limits of SGD》
No 34. 【Facebook出品的视觉问答(VQA)系统】
No 35. 《Differentiable Learning-to-Normalize via Switchable Normalization》
No 36. 《DARTS: Differentiable Architecture Search》
No 37. 准确的说,是审美潜变量约束泛化[嘻嘻] http://t.cn/ReMDDKL //@爱可可-爱生…
No 38. 《Understanding and representing the semantics of large structured documents》
No 39. 《爱可可老师24小时热门分享(2018.7.28)》
No 40. 《Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms》
No 41. 《Where are the Blobs: Counting by Localization with Point Supervision》
No 42. 转发微博
No 43. 欢迎参与!
No 44. 【recurrent sequence models vs. feedforward models】
No 45. Switchable Normalization for object detection GitH…
No 46. “Kaggle比赛实战教程”
No 47. 《UNet++: A Nested U-Net Architecture for Medical Image Segmentation》
No 48. “ICML 2018 Workshop on Deep Generative Models – Invited Speakers(Slides)”
No 49. 【如何选择论文投稿的期刊?】
No 50. 《Generating a Fusion Image: One's Identity and Another's Shape》