We’ve now released the 0.2.0 version of the Python StanfordNLP library! Highlights:
1) Much smaller model sizes (9x smaller for English, 11x for German, ...);
2) Substantial speedup of the neural lemmatizer;
3) Easier-to-use CoreNLP interface. More at:
https://github.com/stanfordnlp/stanfordnlp/releases/tag/v0.2.0

✴️ @AI_Python_EN

Generating Game of Thrones Characters Using StyleGAN
article: https://blog.nanonets.com/stylegan-got/

gitHub repo: https://github.com/iyaja/stylegan-encoder
#deeplearning #ArtificialIntelligence
✴️ @AI_Python_EN

There's increasing interest in time-series analysis in the #DataScience community. I see this as a very positive development, as TSA has seldom gotten the attention from data scientists I feel it merits.

Econometricians and statisticians in other fields such as meteorology have been analyzing time-series data for many decades. Before diving into LSTMs and other machine learning tools - which statisticians also use - I think it would be wise to read up on other methods statisticians employ and what they've learned from more than a century of experience.

Here are some good books on TSA:

- Time Series Analysis (Wei)
- Time Series Analysis and Its Applications (Shumway and Stoffer)
- Forecasting (Hyndman and Athanasopoulos)
- Multiple Time-Series Analysis (Lütkepohl)
- Time Series Analysis by State Space Methods (Durbin and Koopman)
- Time Series Modelling with Unobserved Components (Pelagatti)
- Hidden Markov Models for Time Series (Zucchini)
- GARCH Models (Francq and Zakoïan)
- Nonparametric Econometrics (Li and Racine)

There are academic journals as well. (I subscribed to the Journal of Forecasting and the Journal of Time Series Analysis until they jacked up their subscription fees.)

TSA is a difficult topic, IMO, but more important than ever in the age of big data.
#book
✴️ @AI_Python_EN

Multivariate Data Visualization with python


9 Visualization that useful and doable to any tabular dataset

Try these 9 VisualizationCreated by :Rahul Agarwal

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #python #visualization #visualisation

✴️ @AI_Python_EN

Data Visualization is a very important step in Data Science, so we should try to MASTER it.

Here are the useful links for Data Visualization -

1)Quick and Easy Data Visualizations in Python with Code.
(https://lnkd.in/fXJ-_Y8)

2)10 Useful Python Data Visualization Libraries for Any Discipline.
(https://lnkd.in/fBxbHwr)

3)Top 50 matplotlib Visualizations – The Master Plots (with full python code).
(https://lnkd.in/fGrnGax)

4)Data Visualization Effectiveness Profile.
(https://lnkd.in/f3v52Fd)

5)The Visual Perception of Variation in Data Displays.
(https://lnkd.in/fm-TbPM)

6)Matplotlib Tutorial – A Complete Guide to Python Plot w/ Examples.
(https://lnkd.in/fFkUgQP)

7)Interactive Data Visualization in Python With Bokeh.
(https://lnkd.in/fEfQAvg)

8) Data Visualization in R
https://lnkd.in/fEvZB_N

9) The Next Level of Data Visualization in Python (Plotly)
https://lnkd.in/fKn4cPM

#datascience #dataanalysis #datavisualization #python #r

✴️ @AI_Python_EN

How To Define A Convolutional Layer In PyTorch

http://bit.ly/2JFFlxI
#AI #DeepLearning #MachineLearning
#DataScience


✴️ @AI_Python_EN

📍list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning


🔗 Repo github: https://github.com/hurcy/awesome-ehr-deeplearning

@AI_Python_EN

Some python packages that make #deeplearning easier


✴️ @AI_Python_EN

A Step-by-Step Introduction to Object Detection Algorithms (10 pages) - Pulkit Sharma

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #objectdetection

✴️ @AI_Python_EN

#DataScience Learning Path For Complete Beginners:
https://bit.ly/2JhcjXW
#BigData #MachineLearning #AI #DataScientists #Python

✴️ @AI_Python

Decision Tree Classifier from Scratch
http://bit.ly/2HAW0lh

#MachineLearning

✴️ @AI_Python

✴️ @AI_Python

A Practical Introduction to #DeepLearning with #Python
http://bit.ly/2QbmApb
#MachineLearning
✴️ @AI_Python

There are now many methods we can use when our dependent variable is not continuous. SVM, XGBoost and Random Forests are some popular ones.

There are also "traditional" methods, such as Logistic Regression. These usually scale well and, when used properly, are competitive in terms of predictive accuracy.

They are probabilistic models, which gives them additional flexibility. They also are often easier to interpret, critical when the goal is explanation, not just prediction.

They can be more work, however, and are probably easier to misuse than newer methods such as Random Forests. Here are some excellent books on these methods that may be of interest:

- Categorical Data Analysis (Agresti)
- Analyzing Categorical Data (Simonoff)
- Regression Models for Categorical Dependent Variables (Long and Freese)
- Generalized Linear Models and Extensions (Hardin and Hilbe)
- Regression Modeling Strategies (Harrell)
- Applied Logistic Regression (Hosmer and Lemeshow)
- Logistic Regression Models (Hilbe)
- Analysis of Ordinal Categorical Data (Agresti)
- Applied Ordinal Logistic Regression (Liu)
- Modeling Count Data (Hilbe)
- Negative Binomial Regression (Hilbe)
- Handbook of Survival Analysis (Klein et al.)
- Survival Analysis: A Self-Learning Text (Kleinbaum and Klein)

#statistics #book #Machinelearning

✴️ @AI_Python

New #ArtificialIntelligence Sees Like a Human, Bringing Us Closer to Skynet

Read the research: https://lnkd.in/dU9W3D4

✴️ @AI_Python

Can #neuralnetworks be made to reason?" Conversation with Ian Goodfellow

Full version: https://www.youtube.com/watch?v=Z6rxFNMGdn0


✴️ @AI_Python

Tools like #PyTorch, fast.ai, and open source #datasets are making deep learning faster and more accessible. Learn how one #ML hobbyist used these resources to train a convolutional neural network that can classify gastrointestinal images.

🌎 Link

✴️ @AI_Python

We are open-sourcing Pythia, a #deeplearning platform to support multitasking for vision and language tasks. With Pythia, researchers can more easily build, reproduce, and benchmark AI models.

https://code.fb.com/ai-research/pythia/

✴️ @AI_Python_EN

Course material for STAT 479: #DeepLearning (SS 2019) course at University Wisconsin-Madison


🌎 Learn more

✴️ @AI_Python_EN