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/

❇️ @AI_Python_EN

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

❇️ @AI_Python_EN

"Differentiable Convex Optimization Layers"

CVXPY creates powerful new PyTorch and TensorFlow layers

Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/

#PyTorch #TensorFlow #NeurIPS2019


❇️ @AI_Python_EN

Streamlit, a Python framework that dedicates to deploying Machine Learning and Data Science models. If you are a data scientist who's struggling to showcase your fantastic works, I would encourage you to check it out.

The author had a nice article/ tutorial at Med:

#machinelearning #mlops
#datascience

Github link:
https://github.com/streamlit/streamlit

❇️ @AI_Python_EN

Don't confuse creating a data product and deployment. Streamlit makes creating a GUI-type data product easy for Python, much like shiny makes it easy for R.

Deployment is the next step: running it on a server, having security and user authentication, scaling it as required, updating the application when necessary, etc.

That's what the ownR platform does for shiny and dash already and will soon offer for Streamlit too. If you need to actually deploy your data products, especially in an enterprise environment, drop me an invite or a DM for a demo or a trial account!

❇️ @AI_Python_EN

Data science is not #MachineLearning .
Data science is not #statistics.
Data science is not analytics.
Data science is not #AI.

#DataScience is a process of:
Obtaining your data
Scrubbing / Cleaning your data
Exploring your data
Modeling your data
iNterpreting your data

Data Science is the science of extracting useful information from data using statistics, skills, experience and domain knowledge.

If you love data, you will like this role....

solving business problems using data is data science. Machine learning/statistics /analytics may come as a way of the solution of a particular business problem. Sometimes we may need all to solve a problem and sometimes even a crosstabs may be handy.

➡️ Get free resources at his site:
www.claoudml.com

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What Are "Panel Models?"​ Part 1

In #statistics, the English is sometimes as hard as the math. Vocabulary is frequently used in confusing ways and often differs by discipline. "Panel" and "longitudinal" are two examples - economists tend to favor the first term, while researchers in most other fields use the second to mean essentially the same thing.

But to what "thing" do they refer? Say, for example, households, individual household members, companies or brands are selected and followed over time. Statisticians working in many fields, such as economics and psychology, have developed numerous techniques which allow us to study how these households, household members, companies or brands change over time, and investigate what might have caused these changes.

Marketing mix modeling conducted at the category level is one example that will be close to home for many marketing researchers. In a typical case, we might have four years of weekly sales and marketing data for 6-8 brands in a product or service category. These brands would comprise the panel. This type of modeling is also known as cross-sectional time-series analysis because there is an explicit time component in the modeling. It is just one kind of panel/longitudinal analysis.

Marketing researchers make extensive use of online panels for consumer surveys. Panelists are usually not surveyed on the same topic on different occasions though they can be, in which case we would have a panel selected from an online panel. Some MROCs (aka insights communities) also are large and can be analyzed with these methods.

The reference manual for the Stata statistical software provides an in-depth look at many of these methods, particularly those widely-used in econometrics. I should note that there is a methodological connection with mixed-effects models, which I have briefly summarized here. Mplus is another statistical package which is popular among researchers in psychology, education and healthcare, and its website is another good resource.
Longitudinal/panel modeling has featured in countless papers and conference presentations over the years and is also the subject of many books. Here are some books I have found helpful:

Analysis of Longitudinal Data (Diggle et al.)
Analysis of Panel Data (Hsiao)
Econometric Analysis of Panel Data (Baltagi)
Longitudinal Structural Equation Modeling (Newsom)
Growth Modeling (Grimm et al.)
Longitudinal Analysis (Hoffman)
**Applied Longitudinal Data Analysis for Epidemiology (Twisk)**

Many of these methods can also be performed within a Bayesian statistical framework.

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What Are "Panel Models?" Part 2

Rather than having been displaced by big data, AI and machine learning, these techniques are more valuable than ever because we now have more longitudinal data than ever. Many longitudinal methods are computationally intensive and not suitable for massive data on ordinary computers, but parallel processing and cloud computing will often get around this. I also anticipate that more big data versions of their computational algorithms will be developed over the next few years. Here is one example.

For those new to the subject who would like a quick (if somewhat technical) start, I’ve included some edited entries from the Stata reference manual’s glossary below.

Any copy/paste and editing errors are mine.

Arellano–Bond estimator. The Arellano–Bond estimator is a generalized method of moments (GMM) estimator for linear dynamic panel-data models that uses lagged levels of the endogenous variables as well as first differences of the exogenous variables as instruments. The Arellano–Bond estimator removes the panel-specific heterogeneity by first-differencing the regression equation.

autoregressive process. In autoregressive processes, the current value of a variable is a linear function of its own past values and a white-noise error term.

balanced data. A longitudinal or panel dataset is said to be balanced if each panel has the same number of observations.

between estimator. The between estimator is a panel-data estimator that obtains its estimates by running OLS on the panel-level means of the variables. This estimator uses only the between-panel variation in the data to identify the parameters, ignoring any within-panel variation. For it to be consistent, the between estimator requires that the panel-level means of the regressors be uncorrelated with the panel-specific heterogeneity terms.

correlation structure. A correlation structure is a set of assumptions imposed on the within-panel variance–covariance matrix of the errors in a panel-data model.

cross-sectional data. Cross-sectional data refers to data collected over a set of individuals, such as households, firms, or countries sampled from a population at a given point in time.

cross-sectional time-series data. Cross-sectional time-series data is another name for panel data. The term cross-sectional time-series data is sometimes reserved for datasets in which a relatively small number of panels were observed over many periods. See also panel data.

disturbance term. The disturbance term encompasses any shocks that occur to the dependent variable that cannot be explained by the conditional (or deterministic) portion of the model.

dynamic model. A dynamic model is one in which prior values of the dependent variable or disturbance term affect the current value of the dependent variable.

endogenous variable. An endogenous variable is a regressor that is correlated with the unobservable error term. Equivalently, an endogenous variable is one whose values are determined by the equilibrium or outcome of a structural model.

exogenous variable. An exogenous variable is a regressor that is not correlated with any of the unobservable error terms in the model. Equivalently, an exogenous variable is one whose values change independently of the other variables in a structural model.

fixed-effects model. The fixed-effects model is a model for panel data in which the panel-specific errors are treated as fixed parameters. These parameters are panel-specific intercepts and therefore allow the conditional mean of the dependent variable to vary across panels. The linear fixed effects estimator is consistent, even if the regressors are correlated with the fixed effects.

generalized estimating equations (GEE). The method of generalized estimating equations is used to fit population-averaged panel-data models. GEE extends the GLM method by allowing the user to specify a variety of different within-panel correlation structures.