1 |
What is Natural Language Processing? |
bogdani |
112 |
resource |
20.40 |
2 |
Is it a boy or a girl? An introduction to Machine Learning |
bogdani |
711 |
resource |
20.26 |
3 |
Does the h index have predictive power? |
Jorge E. Hirsch |
999 |
paper |
20.05 |
4 |
Supervised similarity: Learning symmetric relations from duplicate question data |
Matthew Honnibal |
133 |
tutorial |
19.60 |
5 |
Can neural machine translation do simultaneous translation? |
Kyunghyun Cho, Masha Esipova |
999 |
paper |
19.50 |
6 |
Words or Characters? Fine-grained Gating for Reading Comprehension |
Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W Cohen, Ruslan Salakhutdinov |
999 |
paper |
19.42 |
7 |
What Do Recurrent Neural Network Grammars Learn About Syntax? |
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, Noah A Smith |
999 |
paper |
19.42 |
8 |
Do Multi-Sense Embeddings Improve Natural Language Understanding? |
Jiwei Li, Dan Jurafsky |
999 |
paper |
19.42 |
9 |
Do Deep Convolutional Nets Really Need to be Deep and Convolutional? |
Gregor Urban, Krzysztof J Geras, Samira Ebrahimi Kahou, Ozlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matt Richardson |
999 |
paper |
19.38 |
10 |
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment |
Hongyuan Mei, Mohit Bansal, Matthew R. Walter |
999 |
paper |
19.38 |
11 |
When are tree structures necessary for deep learning of representations? |
Jiwei Li, Minh-Thang Luong, Dan Jurafsky, Eudard Hovy |
999 |
paper |
19.38 |
12 |
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings |
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Kalai |
999 |
paper |
19.34 |
13 |
Is language evolution grinding to a halt?: Exploring the life and death of words in English fiction |
Eitan Adam Pechenick, Christopher M. Danforth, Peter Sheridan Dodds |
999 |
paper |
19.30 |
14 |
Is science becoming more interdisciplinary? Measuring and mapping six research fields over time |
Alan L. Porter, Ismael Rafols |
999 |
paper |
19.16 |
15 |
Vanilla Neural Network |
Goku Mohandas |
711 |
tutorial |
18.01 |
16 |
Convolutional Neural Networks |
Goku Mohandas |
744 |
tutorial |
18.01 |
17 |
The e-Index, Complementing the h-Index for Excess Citations |
Chun-Ting Zhang |
999 |
paper |
17.72 |
18 |
Step Forward Feature Selection: A Practical Example in Python |
Matthew Mayo |
262 |
resource |
17.52 |
19 |
Linear Regression |
Goku Mohandas |
102 |
tutorial |
17.50 |
20 |
Image-to-Image Translation in Tensorflow |
Christopher Hesse |
731 |
tutorial |
16.79 |
21 |
Logistic Regression |
Goku Mohandas |
516 |
tutorial |
16.41 |
22 |
How to Escape Saddle Points Efficiently |
Chi Jin*, Rong Ge, Praneeth Netrapalli , Sham M. Kakade, Michael I. Jordan |
187 |
resource |
16.16 |
23 |
Do Altmetrics Work? Twitter and Ten Other Social Web Services |
Mike Thelwall, Stefanie Haustein, Vincent Larivière, Cassidy R. Sugimoto |
999 |
paper |
16.02 |
24 |
Implementing a neural Part-of-Speech tagger |
Jonathan K. Kummerfeld |
215 |
resource |
15.87 |
25 |
Character-level text classification: CNN |
Puya Sharif |
744 |
tutorial |
15.75 |
26 |
State-of-the-art neural coreference resolution for chatbots |
Thomas Wolf |
756 |
tutorial |
15.25 |
27 |
Quadratic entropy and analysis of diversity |
C. R. Rao |
999 |
paper |
15.25 |
28 |
How do we capture structure in relational data? |
Matthew Das Sarma |
233 |
resource |
15.02 |
29 |
The h’-Index, Effectively Improving the h-Index Based on the Citation Distribution |
Chun-Ting Zhang |
999 |
paper |
14.01 |
30 |
Deep Learning and the Future of AI |
Yann LeCun |
811 |
lecture |
13.87 |
31 |
Theano Tutorial |
Colin Raffel |
731 |
tutorial |
13.77 |
32 |
An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship |
J. E. Hirsch |
999 |
paper |
13.62 |
33 |
Clustering text documents using k-means |
F Pdregosa |
57 |
resource |
13.31 |
34 |
Recurrent neural networks and LSTM tutorial in Python and TensorFlow |
Author Unknown |
742 |
tutorial |
13.08 |
35 |
7 types of Artificial Neural Networks for Natural Language Processing |
Data Monsters |
711 |
resource |
12.97 |
36 |
Convolutional Neural Networks Tutorial in TensorFlow |
Andy Thomas |
744 |
tutorial |
12.72 |
37 |
Deep learning tutorial on Caffe technology : basic commands, Python and C++ code |
Christopher Bourez |
731 |
tutorial |
12.64 |
38 |
Building a Logistic Regression in Python, Step by Step |
Susan Li |
516 |
tutorial |
12.64 |
39 |
Building A Logistic Regression in Python, Step by Step |
Susan Li |
516 |
resource |
12.64 |
40 |
Deep Learning chatbots analysis - whats the actual tech behind them? |
Przemyslaw |
756 |
tutorial |
12.33 |
41 |
Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience |
Ismael Rafols, Martin Meyer |
999 |
paper |
12.29 |
42 |
37 Reasons why your Neural Network is not working |
Slav Ivanov |
713 |
tutorial |
12.04 |
43 |
Calculus for Deep Learning |
Various Authors |
711 |
resource |
11.76 |
44 |
TensorFlow Dataset API tutorial – build high performance data pipelines |
Andy |
731 |
resource |
11.76 |
45 |
A Good Part-of-Speech Tagger in about 200 Lines of Python |
Matthew Honnibal |
231 |
tutorial |
11.76 |
46 |
How To Understand Derivatives: The Product, Power & Chain Rules |
Kalid |
101 |
tutorial |
11.56 |
47 |
Python TensorFlow Tutorial – Build a Neural Network |
Andy Thomas |
731 |
tutorial |
10.35 |
48 |
Markov Chain Monte Carlo (MCMC) |
Author Unknown |
221 |
resource |
9.47 |
49 |
TensorFlow Tutorial For Beginners |
Karlijn Willems |
731 |
tutorial |
8.68 |
50 |
TensorFlow Tutorial For Beginners |
Karlijn Willems |
731 |
tutorial |
8.68 |
51 |
TensorFlow Tutorial For Beginners |
Karlijn Willems |
731 |
resource |
8.68 |
52 |
SippyCup Unit 1: Natural language arithmetic |
Bill MacCartney |
365 |
tutorial |
6.65 |
53 |
Train Neural Machine Translation Models with Sockeye |
Felix Hieber, Tobias Domhan |
753 |
tutorial |
5.88 |
54 |
The Traveling Salesperson Problem |
Author Unknown |
104 |
tutorial |
5.66 |
55 |
Recurrent Neural Networks |
Stephen Grossberg |
741 |
paper |
4.70 |