1 |
Neural Information Retrieval: At the End of the Early Years |
Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K |
713 |
resource |
249.66 |

2 |
Codra: A Novel Discriminative Framework for Rhetorical Analysis |
Shafiq Joty, Giuseppe Carenini, Raymond T. Ng |
999 |
paper |
242.12 |

3 |
Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! |
Sebastian Ruder |
641 |
resource |
224.55 |

4 |
Recent Advances in Document Summarization |
Jin-ge Yao, Xiaojun Wan, Jianguo Xiao |
421 |
survey |
220.41 |

5 |
Deep Learning for NLP, advancements and trends in 2017 |
Javier |
711 |
resource |
217.82 |

6 |
A Comparative Analysis of ChatBots APIs |
Author Unknown |
921 |
resource |
209.73 |

7 |
Discriminative Syntax-based Word Ordering for Text Generation |
Yue Zhang, Stephen Clark |
999 |
paper |
208.79 |

8 |
Negated bio-events: analysis and identification |
Raheel Nawaz, Paul Thompson, Sophia Ananiadou |
999 |
paper |
207.70 |

9 |
A Gentle Introduction to Machine Learning |
Author Unknown |
711 |
tutorial |
207.50 |

10 |
Transfer Learning - Machine Learnings Next Frontier |
Sebastian Ruder |
978 |
tutorial |
203.34 |

11 |
Understanding Convolutional Neural Networks for NLP |
Denny Britz |
744 |
tutorial |
200.63 |

12 |
The Definitive Guide to Natural Language Processing |
Javier Couto |
133 |
tutorial |
198.88 |

13 |
Tombones Computer Vision Blog |
Tomasz Malisiewicz |
958 |
resource |
196.39 |

14 |
Introduction to Natural Language Processing (NLP) 2016 |
Matt Kiser |
133 |
tutorial |
195.23 |

15 |
Similarity-driven Semantic Role Induction via Graph Partitioning |
Joel Lang, Mirella Lapata |
999 |
paper |
194.78 |

16 |
Machine Learning for Humans |
Vishal Maini, Samer Sabri |
134 |
tutorial |
192.57 |

17 |
10 Applications of Artificial Neural Networks in Natural Language Processing |
Olga Davydova |
811 |
resource |
191.75 |

18 |
The Neural Network Zoo |
Fjodor Van Veen |
712 |
tutorial |
191.64 |

19 |
An Overview of Multi-Task Learning in Deep Neural Networks |
Sebastian Ruder |
829 |
tutorial |
191.28 |

20 |
A survey of transfer learning |
Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang |
978 |
resource |
190.75 |

21 |
Using Artificial Intelligence to Augment Human Intelligence |
Shan Carter, Michael Nielsen |
811 |
resource |
190.65 |

22 |
Clustering cliques for graph-based summarization of the biomedical research literature |
Han Zhang, Marcelo Fiszman, Dongwook Shin, Bartomiej Wilkowski, Thomas Rindflesch |
999 |
paper |
189.77 |

23 |
Introduction to Visual Question Answering: Datasets, Approaches and Evaluation |
Javier Couto |
411 |
resource |
186.71 |

24 |
Recurrent Neural Networks |
Stephen Grossberg |
741 |
paper |
185.36 |

25 |
Learning AI if You Suck at Math?—?P5?—?Deep Learning and Convolutional Neural Nets in Plain English! |
Daniel Jeffries |
811 |
tutorial |
182.35 |

26 |
DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION |
Denny Britz |
445 |
tutorial |
182.09 |

27 |
An end to end implementation of a Machine Learning pipeline |
Spandan Madan |
107 |
tutorial |
181.84 |

28 |
30 Amazing Applications of Deep Learning |
Yaron Hadad |
711 |
resource |
180.75 |

29 |
DEEP LEARNING FOR CHATBOTS, PART 2 - IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW |
Denny Britz |
445 |
tutorial |
180.70 |

30 |
Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs |
Denny Britz |
741 |
tutorial |
180.12 |

31 |
Applied Deep Learning - Part 4: Convolutional Neural Networks |
Arden Dertat |
744 |
resource |
178.04 |

32 |
A Practitioner's Guide to Natural Language Processing (Part I)?—?Processing & Understanding Text |
Dipanjan (DJ) Sarker |
112 |
resource |
178.01 |

33 |
Gimli: open source and high-performance biomedical name recognition |
David Campos, Sergio Matos, Jose Oliveira |
999 |
paper |
176.48 |

34 |
Deep Learning for NLP Best Practices |
Sebastian Ruder |
713 |
tutorial |
176.21 |

35 |
Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience |
Ismael Rafols, Martin Meyer |
999 |
paper |
175.98 |

36 |
Rohan #2: Artificial intelligence, ?Progress/?Time |
Rohan Kapur |
811 |
tutorial |
175.30 |

37 |
A Microsoft CNTK tutorial in Python – build a neural network |
Andy Thomas |
711 |
tutorial |
175.28 |

38 |
Ideas on interpreting machine learning |
Patrick Hall, Wen Phan, SriSatish Ambati |
134 |
tutorial |
174.99 |

39 |
State-of-the-art neural coreference resolution for chatbots |
Thomas Wolf |
756 |
tutorial |
174.14 |

40 |
Learning AI if You Suck at Math?—?P7?—?The Magic of Natural Language Processing |
Daniel Jeffries |
133 |
tutorial |
174.08 |

41 |
Ultimate Guide to Understand & Implement Natural Language Processing (with codes in Python) |
Shivam Bansal |
131 |
tutorial |
173.93 |

42 |
Transfer Learning: Leverage Insights from Big Data |
Lars Hulstaert |
753 |
resource |
173.82 |

43 |
12 Frequently Asked Questions on Deep Learning (with their answers)! |
Analytics Vidhya Content Team |
711 |
resource |
171.43 |

44 |
Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy, and Theano |
Denny Britz |
741 |
tutorial |
170.69 |

45 |
RNNs in Tensorflow, a Practical Guide and Undocumented Features |
Denny Britz |
741 |
tutorial |
170.59 |

46 |
A survey of cross-lingual embedding models |
Sebastian Ruder |
721 |
tutorial |
169.87 |

47 |
The Unreasonable Effectiveness of Recurrent Neural Networks |
Andrej Karpathy |
741 |
survey |
169.46 |

48 |
Lexicalization and Generative Power in Ccg |
Marco Kuhlmann, Alexander Koller, Giorgio Satta |
999 |
paper |
168.25 |

49 |
Text Classification with TensorFlow Estimators |
Julian Eisenschlos, Sebastian Ruder |
731 |
resource |
167.72 |

50 |
An Intuitive Explanation of Convolutional Neural Networks |
Ujjwal Karn |
744 |
tutorial |
167.62 |

51 |
ResNet, AlexNet, VGG, Inception: Understanding various architectures of Convolutional Networks |
Koustubh |
744 |
resource |
167.48 |

52 |
Visualizing Representations: Deep Learning and Human Beings |
Christopher Olah |
713 |
tutorial |
167.48 |

53 |
Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano |
Denny Britz |
742 |
tutorial |
166.88 |

54 |
Twitter Sentiment Analysis Using Combined LSTM-CNN Models |
Author Unknown |
381 |
resource |
166.31 |

55 |
Multi-Task Learning Objectives for Natural Language Processing |
Author Unknown |
133 |
resource |
165.07 |

56 |
The Building Blocks of Interpretability |
Chris Olah |
614 |
resource |
164.92 |

57 |
Natural Language Processing (NLP) for Computational Social Science |
Cristian Danescu-Niculescu-Mizil, Lillian Lee |
133 |
tutorial |
164.78 |

58 |
An Intuitive Guide to Linear Algebra |
Kalid Azad |
121 |
tutorial |
163.19 |

59 |
Rohan & Lenny #3: Recurrent Neural Networks & LSTMs |
Rohan Kapur |
741 |
tutorial |
163.03 |

60 |
Introduction to Word2Vec |
Skymind |
721 |
tutorial |
162.93 |

61 |
Learning to Segment |
Piotr Dollar |
862 |
tutorial |
162.48 |

62 |
Deep Learning 2: Part 1 Lesson 4 |
Hiromi Suenaga |
711 |
resource |
162.46 |

63 |
Introduction to Learning to Trade with Reinforcement Learning |
Denny Britz |
857 |
resource |
161.98 |

64 |
Architecture of Convolutional Neural Networks (CNNs) demystified |
Dishashree Gupta |
744 |
tutorial |
161.93 |

65 |
A Beginners Guide to Deep Learning |
Kumar Shridhar |
711 |
tutorial |
161.88 |

66 |
A curated list of data science, analysis and visualization tools |
QuantMind |
134 |
resource |
161.16 |

67 |
Text Classifier Algorithms in Machine Learning |
Roman Trusov |
542 |
tutorial |
161.01 |

68 |
Summaries and notes on Deep Learning research papers |
Denny Britz |
713 |
resource |
160.89 |

69 |
Natural Language Processing for Beginners: Using TextBlob |
Shubham Jain |
731 |
resource |
160.83 |

70 |
An Overview of Proxy-label Approaches for Semi-supervised Learning |
Sebastian Ruder |
581 |
resource |
160.53 |

71 |
From Natural Language Processing to Ar4ficial Intelligence |
Jonathan Mugan |
133 |
tutorial |
160.48 |

72 |
What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? |
ffriend |
744 |
tutorial |
160.13 |

73 |
Making computers explain themselves |
Larry Hardesty |
713 |
resource |
159.73 |

74 |
New approaches to Deep Networks:Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious) |
Gideon Kowadlo |
731 |
resource |
159.72 |

75 |
Prodigy: A new tool for radically efficient machine teaching |
Matthew Honnibal, Ines Montani |
134 |
resource |
159.38 |

76 |
Machine Learning Glossary |
Author Unknown |
107 |
resource |
159.05 |

77 |
Simple, Strong Deep-Learning Baselines for NLP in several frameworks |
Dan Pressel |
713 |
library |
158.88 |

78 |
What is machine learning? Everything you need to know |
Nick Heath |
711 |
resource |
158.84 |

79 |
Getting Started with Sentiment Analysis |
bogdani |
381 |
resource |
158.80 |

80 |
TensorFlow RNN Tutorial |
Matt Mollison |
731 |
resource |
158.49 |

81 |
Neural Text Embeddings for IR |
Bhaskar Mitra, Nick Craswell |
721 |
tutorial |
158.20 |

82 |
Comprehensive Guide on t-SNE algorithm with implementation in R & Python |
SAURABH.JAJU2 |
341 |
tutorial |
158.15 |

83 |
Keras and Convolutional Neural Networks (CNNs) |
Adrian Rosebrock |
744 |
resource |
157.84 |

84 |
A Beginner's guide to Recurrent Networks and LSTMs |
Skymind |
742 |
tutorial |
157.82 |

85 |
A social network's changing statistical properties and the quality of human innovation |
Brian Uzzi |
999 |
paper |
157.62 |

86 |
Rohan & Lenny #2: Convolutional Neural Networks |
Lenny Khazan |
744 |
tutorial |
157.54 |

87 |
Transfer Learning |
Niklas Donges |
978 |
resource |
157.37 |

88 |
Machine Learning Basics: a Guide for the Perplexed |
Will Gannon |
134 |
resource |
157.36 |

89 |
A Gentle Introduction to Neural Networks for Machine Learning |
James Le |
711 |
resource |
156.94 |

90 |
Uncovering the Intuition behind Capsule Networks and Inverse Graphics: Part I |
Tanay Kothari |
641 |
resource |
156.83 |

91 |
Uncovering the Intuition behind Capsule Networks and Inverse Graphics |
Tanay Kothari |
711 |
resource |
156.80 |