A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

https://arxiv.org/abs/1910.13148

#MachineLearning #neurips, #NeurIPS2019

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How 20th Century Fox uses ML to predict a movie audience
Google Cloud Blog
http://bit.ly/2N3I7SC

#AI #DeepLearning #MachineLearning #DataScience

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categorical data analysis I've found very helpful:
- Generalized Linear Models and Extensions (Hardin and Hilbe)
- Categorical Data Analysis (Agresti)
- Analyzing Categorical Data (Simonoff)
- Regression Models for Categorical Dependent Variables (Long and Freese)
- Applied Logistic Regression (Hosmer and Lemeshow)
- Logistic Regression Models (Hilbe)
- Analysis of Ordinal Categorical Data (Agresti)
- Applied Ordinal Logistic Regression (Liu)
Surveys also collect data in the form of counts ("How many times have you...").
Analyzing count data with methods designed for continuous data is usually unwise, and here are a couple of excellent books on that topic:
- Modeling Count Data (Hilbe)
- Negative Binomial Regression (Hilbe)
There are numerous "machine learners" I have also used over the years but, in general, they tell us less about the Why, essential in consumer research. Though predictive analytics is not typically the purpose of consumer surveys, used competently, statistical methods are also highly competitive with machine learning in terms of predictive accuracy.

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1) TensorFlow World 2019 Keynote
https://www.youtube.com/watch?v=MunFeX-0MD8

2) Introduction to TensorFlow 2.0: Easier for beginners, and more powerful for experts (TF World '19)
https://www.youtube.com/watch?v=5ECD8J3dvDQ&t=966s

3) Swift for TensorFlow (TF World '19)
https://www.youtube.com/watch?v=9FWsSGD6V8Q

4) Building models with tf.text (TF World '19)
https://www.youtube.com/watch?v=iu_OSAg5slY&t=1s

5) Performant, scalable models in TensorFlow 2 with tf.data, tf.function & tf.distribute (TF World '19)
https://www.youtube.com/watch?v=yH1cF7GnoIo&t=5s

6) Getting involved in the TensorFlow Community (TF World '19)
https://www.youtube.com/watch?v=UbWGYcTUPyI&t=16s

7) TensorFlow World 2019 | Day 1 Livestream
https://www.youtube.com/watch?v=MgrTRK5bbsg

8) Great TensorFlow Research Cloud projects from around the world (TF World '19)
https://www.youtube.com/watch?v=rkqukapSmwQ&t=13s

9) TensorFlow Lite: Solution for running ML on-device (TF World '19)
https://www.youtube.com/watch?v=0SpZy7iouFU

10) TensorFlow Model Optimization: Quantization and Pruning (TF World '19)
https://www.youtube.com/watch?v=3JWRVx1OKQQ&t=1s

11) TFX: Production ML Pipelines with TensorFlow (TF World '19)
https://www.youtube.com/watch?v=TA5kbFgeUlk&t=1452s

12) TensorFlow World 2019 | Day 2 Livestream PM
https://www.youtube.com/watch?v=gy6v-Vc_P0U

13) Unlocking the power of ML for your JavaScript applications with TensorFlow.js (TF World '19)
https://www.youtube.com/watch?v=kKp7HLnPDxc

14) Day 2 Keynote (TF World '19)
https://www.youtube.com/watch?v=zxd3Q2gdArY

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Spiking Neural Network (SNN) with PyTorch: towards bridging the gap between deep learning and the human brain

https://guillaume-chevalier.com/spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp-hebbian-learning/


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If you have any technical skills in machine learning, data science, natural language processing, deep learning, etc. and are interested in paid (remote) mini-projects and gigs on the side,
then this is a good opportunity to get compensated while further sharpening your skills on your own schedule.

IMHO also useful if you're a grad student, have student loans, or just want to build up your portfolio.

If you're interested, please opt in here:

Feel free to email gr2511@columbia.edu for any questions.

Deep Learning course: lecture slides and lab notebooks

https://m2dsupsdlclass.github.io/lectures-labs/

#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning #NeuralNetworks

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ICCV19 Best Paper Award

SinGAN: Learning a Generative Model from a Single Natural Image

https://www.google.com/amp/s/syncedreview.com/2019/11/03/iccv-2019-and-corl-2019-announce-best-papers-deepmind-alphastar-reaches-grandmaster-level/amp/

ICCV 2019 and CoRL 2019 Announce Best Papers; DeepMind AlphaStar Reaches ‘Grandmaster Level’

https://www.google.com/amp/s/syncedreview.com/2019/11/03/iccv-2019-and-corl-2019-announce-best-papers-deepmind-alphastar-reaches-grandmaster-level/amp/

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ICCV 2019 | Best Paper Award: SinGAN: Learning a Generative Model from a Single Natural Image
https://lnkd.in/fS3ZBAP

ICCV 2019 | Best Student Paper Award: PLMP — Point-Line Minimal Problems in Complete Multi-View Visibility
https://lnkd.in/f7CDuq2

ICCV 2019 | Best Paper Honorable Mentions
Paper: Asynchronous Single-Photon 3D Imaging
https://lnkd.in/fMpQPCj

Paper: Specifying Object Attributes and Relations in Interactive Scene Generation
https://lnkd.in/fmjk9eZ

You can find all papers on the ICCV 2019 open access website:
https://lnkd.in/gaBwvS4

Source: Synced

#machinelearning #deeplearning #computervision #iccv2019

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#ArtificialIntelligence #machinelearning

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20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation Metrics
#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/34aNEKN

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What's the purpose of statistics?

"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek

The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.

These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.

How does it do this?

One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.

Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).

Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.

These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.

Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.

Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.

Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.

The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.

"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey

#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience

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What is a Time Series?

Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research. Unfortunately, most Marketing Researchers and Data Scientists still have had little exposure to it. As we'll see, it has many very important applications for marketers.

Just to get our terms straight, below is a simple illustration of what a time series data file looks like. The column labeled DATE is the date variable and corresponds to a respondent ID in survey research data. WEEK, the sequence number of each week, is included because using this column rather than the actual dates can make graphs less cluttered. The sequence number can also serve as a trend variable in certain kinds of time series models.

I should note that the unit of analysis doesn't have to be brands and can include individual consumers or groups of consumers whose behavior is followed over time.

But first, why do we need to distinguish between cross-sectional and time series analysis? For several reasons, one being that our research objectives will usually be different. Another is that most statistical methods we learn in college and make use of in marketing research are intended for cross-sectional data, and if we apply them to time series data the results we obtain may be misleading. Time is a dimension in the data we need to take into account.

Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data:
1- Standard errors can be far off. More often than not, p-values will be too small and variables can appear "more significant" than they really are;
2- In some cases regression coefficients can be seriously biased; and
3- We are not taking advantage of the information the serial correlation in the data provides.

Univariate Analysis
To return to our example data, one objective might be to forecast sales for our brand. There are many ways to do this and the most straightforward is univariate analysis, in which we essentially extrapolate future data from past data. Two popular univariate time series methods are Exponential Smoothing (e.g., Holt-Winters) and ARIMA (Autoregressive Integrated Moving Average). Causal Modeling

Obviously, there are risks in assuming the future will be like the past but, fortunately, we can also include "causal" (predictor) variables to help mitigate these risks. But besides improving the accuracy of our forecasts, another objective may be to understand which marketing activities most influence sales.

Causal variables will typically include data such as GRPs and price and also may incorporate data from consumer surveys or exogenous variables such as GDP. These kinds of analyses are called Market Response or Marketing Mix modeling and are a central component of ROMI (Return on Marketing Investment) analysis. They can be thought of as key driver analysis for time series data. The findings are often used in simulations to try to find the "optimal" marketing mix.

Transfer Function Models, ARMAX and Dynamic Regression are terms that refer to specialized regression procedures developed for time series data. There are more sophisticated methods, in addition, and I'll touch on a few in just a bit.

Part 1

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What is a Time Series?

Multiple Time Series

You might need to analyze multiple time series simultaneously, e.g., sales of your brands and key competitors. Figure 2 below is an example and shows weekly sales data for three brands over a one-year period. Since sales movements of brands competing with each other will typically be correlated over time, it often will make sense, and be more statistically rigorous, to include data for all key brands in one model instead of running separate models for each brand.

Vector Autoregression (VAR), the Vector Error Correction Model (VECM) and the more general State Space framework are three frequently-used approaches to multiple time series analysis. Causal data can be included and Market Response/Marketing Mix modeling conducted.

Other Methods

There are several additional methods relevant to marketing research and data science I'll now briefly describe.

Panel Models include cross sections in a time series analysis. Sales and marketing data for several brands, for instance, can be stacked on top of one another and analyzed simultaneously. Panel modeling permits category-level analysis and also comes in handy when data are infrequent (e.g., monthly or quarterly).

Longitudinal Analysis is a generic and sometimes confusingly-used term that can refer to Panel modeling with a small number of periods ("short panels"), as well as to Repeated Measures, Growth Curve Analysis or Multilevel Analysis. In a literal sense it subsumes time series analysis but many authorities reserve that term for analysis of data with many time periods (e.g., >25). Structural Equation Modeling (SEM) is one method widely-used in Growth Curve modeling and other longitudinal analyses.

Survival Analysis is a branch of #statistics for analyzing the expected length of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. It's also called Duration Analysis in Economics and Event History Analysis in Sociology. It is often used in customer churn analysis.
In some instances one model will not fit an entire series well because of structural changes within the series, and model parameters will vary across time. There are numerous breakpoint tests and models (e.g., State Space, Switching Regression) available for these circumstances.

You may also notice that sales, call center activity or other data series you are tracking exhibit clusters of volatility. That is, there may be periods in which the figures move up and down in much more extreme fashion than other periods.

In these cases, you should consider a class of models with the forbidding name of GARCH (Generalized Autoregressive Conditional Heteroskedasticity). ARCH and GARCH models were originally developed for financial markets but can used for other kinds of time series data when volatility is of interest. Volatility can fall into many patterns and, accordingly, there are many flavors of GARCH models. Causal variables can be included. There are also multivariate extensions (MGARCH) if you have two or more series you wish to analyze jointly.

Non-Parametric Econometrics is a very different approach to studying time series and longitudinal data that is now receiving a lot of attention because of #bigdata and the greater computing power we now enjoy. These methods are increasingly feasible and useful as alternatives to the more familiar methods such as those described in this article.

#MachineLearning (e.g., #ArtificialNeuralNetwork s) is also useful in some circumstances but the results can be hard to interpret - they predict well but may not help us understand the mechanism that generated to data (the Why). To some extent, this drawback also applies to non-parametric techniques.

Most of the methods I've mentioned are Time Domain techniques. Another group of methods known as Frequency Domain, plays a more limited role in Marketing Research.

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New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0

- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code

http://pyimg.co/5wzc5

#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision

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