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11,700 | generative deep learning stochasticity is good to induce robustness because gan training results in dynamic equilibriumgans are likely to get stuck in all sorts of ways introducing randomness during training helps prevent this we introduce randomness in two waysby using dropout in the discriminator and by adding random... |
11,701 | introduction to generative adversarial networks generator_input keras input(shape=(latent_dim,) layers dense( )(generator_inputx layers leakyrelu()(xx layers reshape(( ))(xtransforms the input into -channel feature map layers conv ( padding='same')(xx layers leakyrelu()(xx layers conv dtranspose( strides= padding='same... |
11,702 | generative deep learning the adversarial network finallyyou'll set up the ganwhich chains the generator and the discriminator when trainedthis model will move the generator in direction that improves its ability to fool the discriminator this model turns latent-space points into classification decision--"fakeor "real"-... |
11,703 | introduction to generative adversarial networks x_train x_train[y_train flatten(= selects frog images (class x_train x_train reshape(x_train shape[ ],(heightwidthchannels)astype('float ' iterations batch_size save_dir 'your_dirnormalizes data specifies where you want to save generated images start for step in range(ite... |
11,704 | generative deep learning when trainingyou may see the adversarial loss begin to increase considerablywhile the discriminative loss tends to zero--the discriminator may end up dominating the generator if that' the casetry reducing the discriminator learning rateand increase the dropout rate of the discriminator figure p... |
11,705 | summary with creative applications of deep learningdeep networks go beyond annotating existing content and start generating their own you learned the followinghow to generate sequence dataone timestep at time this is applicable to text generation and also to note-by-note music generation or any other type of timeseries... |
11,706 | this covers important takeaways from this book the limitations of deep learning the future of deep learningmachine learningand ai resources for learning further and working in the field you've almost reached the end of this book this last will summarize and review core concepts while also expanding your horizons beyond... |
11,707 | key concepts in review this section briefly synthesizes the key takeaways from this book if you ever need quick refresher to help you recall what you've learnedyou can read these few pages various approaches to ai first of alldeep learning isn' synonymous with ai or even with machine learning artificial intelligence is... |
11,708 | conclusions image classificationvastly improved machine translationand more the hype may (and likely willrecedebut the sustained economic and technological impact of deep learning will remain in that sensedeep learning could be analogous to the internetit may be overly hyped up for few yearsbut in the longer term it wi... |
11,709 | that' the magic of deep learningturning meaning into vectorsinto geometric spacesand then incrementally learning complex geometric transformations that map one space to another all you need are spaces of sufficiently high dimensionality in order to capture the full scope of the relationships found in the original data ... |
11,710 | conclusions in the futuredeep learning will not only be used by specialists--researchersgraduate studentsand engineers with an academic profile--but will also be tool in the toolbox of every developermuch like web technology today everyone needs to build intelligent appsjust as every business today needs websiteevery p... |
11,711 | be aware of validation-set overfitting when turning hyperparametersthe fact that your hyperparameters may end up being overspecialized to the validation set avoiding this is the purpose of having separate test setkey network architectures the three families of network architectures that you should be familiar with are ... |
11,712 | conclusions rememberto perform binary classificationend your stack of layers with dense layer with single unit and sigmoid activationand use binary_crossentropy as the loss your targets should be either or from keras import models from keras import layers model models sequential(model add(layers dense( activation='relu... |
11,713 | convnets convolution layers look at spatially local patterns by applying the same geometric transformation to different spatial locations (patchesin an input tensor this results in representations that are translation invariantmaking convolution layers highly data efficient and modular this idea is applicable to spaces... |
11,714 | conclusions point in geometric space of statesthey should be used preferentially over convnets in the case of sequences where patterns of interest aren' invariant by temporal translation (for instancetimeseries data where the recent past is more important than the distant pastthree rnn layers are available in kerassimp... |
11,715 | mapping vector data to vector data predictive healthcare--mapping patient medical records to predictions of patient outcomes behavioral targeting--mapping set of website attributes with data on how long user will spend on the website product quality control--mapping set of attributes relative to an instance of manufact... |
11,716 | conclusions mapping images and text to text visual qa --mapping images and natural-language questions about the contents of images to natural-language answers mapping video and text to text video qa --mapping short videos and natural-language questions about the contents of videos to natural-language answers almost any... |
11,717 | the limitations of deep learning the space of applications that can be implemented with deep learning is nearly infinite and yetmany applications are completely out of reach for current deeplearning techniques--even given vast amounts of human-annotated data sayfor instancethat you could assemble dataset of hundreds of... |
11,718 | conclusions figure failure of an image-capt ioning syst em based on deep learning the boy is holding baseball bat in particularthis is highlighted by adversarial exampleswhich are samples fed to deep-learning network that are designed to trick the model into misclassifying them you're already aware thatfor instanceit' ... |
11,719 | the limitations of deep learning in shortdeep-learning models don' have any understanding of their input--at leastnot in human sense our own understanding of imagessoundsand language is grounded in our sensorimotor experience as humans machine-learning models have no access to such experiences and thus can' understand ... |
11,720 | conclusions before--like picturing horse wearing jeansfor instanceor imagining what we' do if we won the lottery this ability to handle hypotheticalsto expand our mental model space far beyond what we can experience directly--to perform abstraction and reasoning--is arguably the defining characteristic of human cogniti... |
11,721 | extreme generalizationquickly adapting to radically novel situations and planning for long-term future situations wrapping up here' what you should rememberthe only real success of deep learning so far has been the ability to map space to space using continuous geometric transformgiven large amounts of human-annotated ... |
11,722 | conclusions the future of deep learning this is more speculative section aimed at opening horizons for people who want to join research program or begin doing independent research given what we know of how deep nets worktheir limitationsand the current state of the research landscapecan we predict where things are head... |
11,723 | they're geometric transformations repeatedly applied inside for loop the temporal for loop is itself hardcoded by human developersit' built-in assumption of the network naturallyrnns are still extremely limited in what they can representprimarily because each step they perform is differentiable geometric transformation... |
11,724 | modular task-level program learned on the fly to solve specific task geometric subroutine conclusions data and feedback algorithmic subroutine task # actions geometric subroutine algorithmic subroutine figure learned program relying on both geomet ric primit ives (patt ern recognit ionint uit ionand algorithmic primiti... |
11,725 | currentlymost of the job of deep-learning engineer consists of munging data with python scripts and then tuning the architecture and hyperparameters of deep network at length to get working model--or even to get state-of-the-art modelif the engineer is that ambitious needless to saythat isn' an optimal setup but ai can... |
11,726 | conclusions remarkable observation has been made repeatedly in recent yearstraining the same model to do several loosely connected tasks at the same time results in model that' better at each task for instancetraining the same neural machine-translation model to perform both english-to-german translation and french-to-... |
11,727 | the future of deep learning global library of abstract subroutines geometric subroutine algorithmic subroutine algorithmic subroutine geometric subroutine algorithmic subroutine algorithmic subroutine geometric subroutine algorithmic subroutine algorithmic subroutine push reusable subroutines fetch relevant subroutines... |
11,728 | conclusions the system will be able to assemble new working model appropriate for the task using very little datathanks to rich program-like primitives that generalize welland extensive experience with similar tasks in the same wayhumans can quickly learn to play complex new video game if they have experience with many... |
11,729 | staying up to date in fast-moving field as final parting wordsi want to give you some pointers about how to keep learning and updating your knowledge and skills after you've turned the last page of this book the field of modern deep learningas we know it todayis only few years olddespite longslow prehistory stretching ... |
11,730 | conclusions it' difficultand becoming increasingly more soto find the signal in the noise currentlythere isn' good solution to this problem but some tools can helpan auxiliary website called arxiv sanity preserver (recommendation engine for new papers and can help you keep track of new developments within specific narr... |
11,731 | final words this is the end of deep learning with pythoni hope you've learned thing or two about machine learningdeep learningkerasand maybe even cognition in general learning is lifelong journeyespecially in the field of aiwhere we have far more unknowns on our hands than certitudes so please go on learningquestioning... |
11,732 | installing keras and its dependencies on ubuntu the process of setting up deep-learning workstation is fairly involved and consists of the following stepswhich this appendix will cover in detail install the python scientific suite--numpy and scipy--and make sure you have basic linear algebra subprogram (blaslibrary ins... |
11,733 | pyt hon vs pyt hon by defaultubuntu uses python when it installs python packages such as pythonpip if you wish to use python insteadyou should use the python prefix instead of python for instancesudo apt-get install python -pip python -dev when you're installing packages using pipkeep in mind that by defaultit targets ... |
11,734 | appendix installing keras and its dependencies on ubuntu setting up gpu support using gpu isn' strictly necessarybut it' strongly recommended all the code examples found in this book can be run on laptop cpubut you may sometimes have to wait for several hours for model to traininstead of mere minutes on good gpu if you... |
11,735 | install tensorflowa tensorflow with or without gpu support can be installed from pypi using pip here' the command without gpu supportsudo pip install tensorflow here' the command to install tensorflow with gpu supportsudo pip install tensorflow-gpu installing theano (optionalbecause you've already installed tensorflowy... |
11,736 | appendix installing keras and its dependencies on ubuntu "image_data_format""channels_last""epsilon" - "floatx""float ""backend""tensorflowwhile the keras script examples/mnist_cnn py is runningyou can monitor gpu utilization in different shell windowwatch - nvidia-smi - --display=utilization you're all setcongratulati... |
11,737 | running jupyter notebooks on an ec gpu instance this appendix provides step-by-step guide to running deep-learning jupyter notebooks on an aws gpu instance and editing the notebooks from anywhere in your browser this is the perfect setup for deep-learning research if you don' have gpu on your local machine the original... |
11,738 | appendix running jupyter notebooks on an ec gpu instance why would you not want to use jupyter on aws for deep learningaws gpu instances can quickly become expensive the one we suggest using costs $ per hour this is fine for occasional usebut if you're going to run experiments for several hours per day every daythen yo... |
11,739 | figure the ec deep learning ami select the xlarge instance (see figure this instance type provides access to single gpu and costs $ per hour of usage (as of march figure the xlarge instance you can keep the default configuration for the steps configure instanceadd storageand add tagsbut you'll customize the configure s... |
11,740 | figure appendix running jupyter notebooks on an ec gpu instance configure new security group note at the end of the launch processyou'll be asked if you want to create new connection keys or if you want to reuse existing keys if you've never used ec beforecreate new keys and download them to connect to your instancesel... |
11,741 | setting up an aws gpu instance create new ssl certificate using openssland create cert key and cert pem files in the current ssl directoryopenssl req - -nodes -days -newkey rsa: -keyout "cert key-out "cert pem-batch configuring jupyter before you use jupyteryou need to touch up its default configuration follow these st... |
11,742 | appendix running jupyter notebooks on an ec gpu instance in case you aren' accustomed to using viremember that you need to press to begin inserting content when you're finishedpress escenter :wqand press enter to quit vi and save your changes (:wq stands for write-quitnote installing keras you're almost ready to start ... |
11,743 | you should see the safety warning shown in figure this warning is due to the fact that the ssl certificate you generated isn' verified by trusted authority (obviously--you generated your ownclick advancedand proceed to navigate figure safet warning you can ignore you should be prompted to enter your jupyter password yo... |
11,744 | |
11,745 | symbols operator operator numerics tensors see scalars convolutions - poolingfor sequence data tensors see vectors tensors see matrices embeddings activation activation functions ad targeting add_loss method admm (alternating direction method of multipliers adversarial networks see also generative deep learninggenerati... |
11,746 | cern channels axis channels-first convention channels-last convention character-level neural language model ciresandan class activationvisualizing heatmaps of - classes classification cloudrunning jobs in clustering cnns see convnets (convolutional neural networkscntk (microsoft cognitive toolkit compilation step conce... |
11,747 | index deep learninglimitations of (continuedrisk of anthropomorphizing machine-learning models - overview - - possible uses of - reasons for interest in - see also generative deep learning deep learning amiec deepdream technique - overview implementing in keras - deepmindgoogle dense layers - dense sampling densely con... |
11,748 | glove (global vectors for word representationdownloading word embeddings loading embeddings in models goodfellowian gpus (graphics processing unitsinstalling on aws instanceson aws - overview selecting - supportsetting up on ubuntu - gradient boosting machines - gradient descent gradient propagation gradient-based opti... |
11,749 | keras framework - cntk developing with - running tensorflow theano keras library keras applications module keras callbacks module keras preprocessing image kernel methods - kernel trick -fold validationiterated with shuffling - kingmadiederik krizhevskyalex regularization regularization label lambda layer language mode... |
11,750 | mini-batch mini-batch sgd (mini-batch stochastic gradient descent minskymarvin mnist dataset model checkpointing model class model depth model plot model fit(function model fit_generator(function modelcheckpoint callbacks models architecture patterns - batch normalization - depthwise separable convolution - residual co... |
11,751 | movielens data set measuring rating disagreement us baby names - analyzing naming trends conclusions and the path ahead ipythonan interactive computing and development environment ipython basics tab completion introspection the %run command executing code from the clipboard keyboard shortcuts exceptions and tracebacks ... |
11,752 | basic indexing and slicing boolean indexing fancy indexing transposing arrays and swapping axes universal functionsfast element-wise array functions data processing using arrays expressing conditional logic as array operations mathematical and statistical methods methods for boolean arrays sorting unique and other set ... |
11,753 | integer indexing panel data data loadingstorageand file formats reading and writing data in text format reading text files in pieces writing data out to text format manually working with delimited formats json data xml and htmlweb scraping binary data formats using hdf format reading microsoft excel files interacting w... |
11,754 | brief matplotlib api primer figures and subplots colorsmarkersand line styles tickslabelsand legends annotations and drawing on subplot saving plots to file matplotlib configuration plotting functions in pandas line plots bar plots histograms and density plots scatter plots plotting mapsvisualizing haiti earthquake cri... |
11,755 | date and time data types and tools converting between string and datetime time series basics indexingselectionsubsetting time series with duplicate indices date rangesfrequenciesand shifting generating date ranges frequencies and date offsets shifting (leading and laggingdata time zone handling localization and convers... |
11,756 | advanced numpy ndarray object internals numpy dtype hierarchy advanced array manipulation reshaping arrays versus fortran order concatenating and splitting arrays repeating elementstile and repeat fancy indexing equivalentstake and put broadcasting broadcasting over other axes setting array values by broadcasting advan... |
11,757 | |
11,758 | the scientific python ecosystem of open source libraries has grown substantially over the last years by late had long felt that the lack of centralized learning resources for data analysis and statistical applications was stumbling block for new python programmers engaged in such work key projects for data analysis (es... |
11,759 | using code examples this book is here to help you get your job done in generalyou may use the code in this book in your programs and documentation you do not need to contact us for permission unless you're reproducing significant portion of the code for examplewriting program that uses several chunks of code from this ... |
11,760 | please address comments and questions concerning this book to the publishero'reilly mediainc gravenstein highway north sebastopolca (in the united states or canada(international or local(faxwe have web page for this bookwhere we list errataexamplesand any additional information you can access this page at to comment or... |
11,761 | |
11,762 | preliminaries what is this book aboutthis book is concerned with the nuts and bolts of manipulatingprocessingcleaningand crunching data in python it is also practicalmodern introduction to scientific computing in pythontailored for data-intensive applications this is book about the parts of the python language and libr... |
11,763 | for many people (myself among them)the python language is easy to fall in love with since its first appearance in python has become one of the most popular dynamicprogramming languagesalong with perlrubyand others python and ruby have become especially popular in recent years for building websites using their numerous ... |
11,764 | people are increasingly finding is that python is suitable language not only for doing research and prototyping but also building the production systemstoo believe that more and more companies will go down this path as there are often significant organizational benefits to having both scientists and technologists using... |
11,765 | numpyshort for numerical pythonis the foundational package for scientific computing in python the majority of this book will be based on numpy and libraries built on top of numpy it providesamong other thingsa fast and efficient multidimensional array object ndarray functions for performing element-wise computations wi... |
11,766 | and tools well-suited for working with financial data in facti initially designed pandas as an ideal tool for financial data analysis applications for users of the language for statistical computingthe dataframe name will be familiaras the object was named after the similar data frame object they are not the samehoweve... |
11,767 | scipy is collection of packages addressing number of different standard problem domains in scientific computing here is sampling of the packages includedscipy integratenumerical integration routines and differential equation solvers scipy linalglinear algebra routines and matrix decompositions extending beyond those pr... |
11,768 | ipython notebook dependenciestornado and pyzmq these are included in epdfree pandas (version or higherat some point while reading you may wish to install one or more of the following packagesstatsmodelspytablespyqt (or equivalentlypyside)xlrdlxmlbasemappymongoand requests these are used in various examples installing t... |
11,769 | need to clean up your windows environment variables on windows you can start typing "environment variablesin the programs search field and select edit environ ment variables for your account on windows xpyou will have to go to control panel system advanced environment variables on the window that pops upyou are looking... |
11,770 | apple os to get started on os xyou must first install xcodewhich includes apple' suite of software development tools the necessary component for our purposes is the gcc and +compiler suite the xcode installer can be found on the os install dvd that came with your computer or downloaded from apple directly once you've i... |
11,771 | : ~/virtualbox vms/windowsxp ipython python |epd_free ( -bit)(defaultapr : : type "copyright""creditsor "licensefor more information ipython -an enhanced interactive python -introduction and overview of ipython' features %quickref -quick reference help -python' own help system object-details about 'object'use 'object??... |
11,772 | gccbut others may not on debian systemsyou can install gcc by executingsudo apt-get install gcc if you type gcc on the command line it should say something likegcc gccno input files nowtime to install pandaseasy_install pandas if you installed epdfree as rootyou may need to add sudo to the command and enter the sudo or... |
11,773 | outside of an internet searchthe scientific python mailing lists are generally helpful and responsive to questions some ones to take look at arepydataa google group list for questions related to python for data analysis and pandas pystatsmodelsfor statsmodels or pandas-related questions numpy-discussionfor numpy-relate... |
11,774 | most of the code examples in the book are shown with input and output as it would appear executed in the ipython shell in [ ]code out[ ]output at timesfor claritymultiple code examples will be shown side by side these should be read left to right and executed separately in [ ]code out[ ]output in [ ]code out[ ]output d... |
11,775 | description of an algorithm or process that takes code-like form while likely not being actual valid source code syntactic sugar programming syntax which does not add new featuresbut makes something more convenient or easier to type acknowledgements it would have been difficult for me to write this book without the sup... |
11,776 | to always follow my dreams and to never settle for less acknowledgements www it-ebooks info |
11,777 | |
11,778 | introductory examples this book teaches you the python tools to work productively with data while readers may have many different end goals for their workthe tasks required generally fall into number of different broad groupsinteracting with the outside world reading and writing with variety of file formats and databas... |
11,779 | web data known as jsonwhich stands for javascript object notation for exampleif we read just the first line of file you may see something like in [ ]path 'ch /usagov_bitly_data- txtin [ ]open(pathreadline(out[ ]'" ""mozilla\\/ (windows nt wow applewebkit\\/ (khtmllike geckochrome\\ safari\\/ "" ""us""nk" "tz""america\\... |
11,780 | now easy to access individual values within records by passing string for the key you wish to accessin [ ]records[ ]['tz'out[ ] 'america/new_yorkthe here in front of the quotation stands for unicodea standard form of string encoding note that ipython shows the time zone string object representation here rather than its... |
11,781 | if in countscounts[ + elsecounts[ return counts if you know bit more about the python standard libraryyou might prefer to write the same thing more brieflyfrom collections import defaultdict def get_counts (sequence)counts defaultdict(intvalues will initialize to for in sequencecounts[ + return counts put this logic in... |
11,782 | which makes this task lot easierin [ ]from collections import counter in [ ]counts counter(time_zonesin [ ]counts most_common( out[ ][( 'america/new_york' )( '' )( 'america/chicago' )( 'america/los_angeles' )( 'america/denver' )( 'europe/london' )( 'asia/tokyo' )( 'pacific/honolulu' )( 'europe/madrid' )( 'america/sao_p... |
11,783 | non-null values dtypesfloat ( )object( in [ ]frame['tz'][: out[ ] america/new_york america/denver america/new_york america/sao_paulo america/new_york america/new_york europe/warsaw nametz the output shown for the frame is the summary viewshown for large dataframe objects the series object returned by frame['tz'has meth... |
11,784 | asia/tokyo pacific/honolulu europe/madrid making horizontal bar plot can be accomplished using the plot method on the counts objectsin [ ]tz_counts[: plot(kind='barh'rot= see figure - for the resulting figure we'll explore more tools for working with this kind of data for examplethe field contains information about the... |
11,785 | out[ ]mozilla/ mozilla/ googlemaps/rochesterny opera/ test_internet_agent googleproducer mozilla/ blackberry nowsuppose you wanted to decompose the top time zones into windows and nonwindows users as simplificationlet' say that user is on windows if the string 'windowsis in the agent string since some of the agents are... |
11,786 | america/argentina/mendoza finallylet' select the top overall time zones to do soi construct an indirect index array from the row counts in agg_countsuse to sort in ascending order in [ ]indexer agg_counts sum( argsort(in [ ]indexer[: out[ ]tz africa/cairo africa/casablanca africa/ceuta africa/johannesburg africa/lusaka... |
11,787 | figure - percentage windows and non-windows users in top-occurring time zones all of the methods employed here will be examined in great detail throughout the rest of the book movielens data set grouplens research ( introductory examples www it-ebooks info |
11,788 | demographic data about the users (agezip codegenderand occupationsuch data is often of interest in the development of recommendation systems based on machine learning algorithms while will not be exploring machine learning techniques in great detail in this booki will show you how to slice and dice data sets like these... |
11,789 | father of the bride part ii ( comedy in [ ]ratings out[ ]int index entries to data columnsuser_id non-null values movie_id non-null values rating non-null values timestamp non-null values dtypesint ( note that ages and occupations are coded as integers indicating groups described in the data set' readme file analyzing ... |
11,790 | is straightforward once you build some familiarity with pandas to get mean movie ratings for each film grouped by genderwe can use the pivot_table methodin [ ]mean_ratings data pivot_table('rating'rows='title'cols='gender'aggfunc='mean'in [ ]mean_ratings[: out[ ]gender title $ , , duck ( 'night mother ( 'til there was ... |
11,791 | non-null values non-null values dtypesfloat ( to see the top films among female viewerswe can sort by the column in descending orderin [ ]top_female_ratings mean_ratings sort_index(by=' 'ascending=falsein [ ]top_female_ratings[: out[ ]gender close shavea ( wrong trousersthe ( sunset blvd ( sunset boulevard( wallace gro... |
11,792 | preferred by men that women didn' rate as highlyreverse order of rowstake first rows in [ ]sorted_by_diff[::- ][: out[ ]gender goodthe bad and the uglythe ( kentucky fried moviethe ( dumb dumber ( longest daythe ( cable guythe ( evil dead ii (dead by dawn( hiddenthe ( rocky iii ( caddyshack ( for few dollars more ( por... |
11,793 | the united states social security administration (ssahas made available data on the frequency of baby names from through the present hadley wickhaman author of several popular packageshas often made use of this data set in illustrating data manipulation in in [ ]names head( out[ ]name sex births mary anna emma elizabet... |
11,794 | mary, , anna, , emma, , elizabeth, , minnie, , margaret, , ida, , alice, , bertha, , sarah, , as this is nicely comma-separated formit can be loaded into dataframe with pandas read_csvin [ ]import pandas as pd in [ ]names pd read_csv('names/yob txt'names=['name''sex''births']in [ ]names out[ ]int index entries to data ... |
11,795 | names pd concat(piecesignore_index=truethere are couple things to note here firstremember that concat glues the dataframe objects together row-wise by default secondlyyou have to pass ignore_index=true because we're not interested in preserving the original row numbers returned from read_csv so we now have very large d... |
11,796 | remember that because births is of integer typewe have to cast either the numerator or denominator to floating point to compute fraction (unless you are using python !the resulting complete data set now has the following columnsin [ ]names out[ ]int index entries to data columnsname non-null values sex non-null values ... |
11,797 | top grouped apply(get_top if you prefer do-it-yourself approachyou could also dopieces [for yeargroup in names groupby(['year''sex'])pieces append(group sort_index(by='births'ascending=false)[: ]top pd concat(piecesignore_index=truethe resulting data set is now quite bit smallerin [ ]top out[ ]int index entries to data... |
11,798 | have grown out of favor with the american population but the story is actually more complicated than thatas will be explored in the next section figure - few boy and girl names over time measuring the increase in naming diversity one explanation for the decrease in plots above is that fewer parents are choosing common ... |
11,799 | non-null values sex non-null values births non-null values year non-null values prop non-null values dtypesfloat ( )int ( )object( figure - proportion of births represented in top names by sex after sorting prop in descending orderwe want to know how many of the most popular names it takes to reach you could write for ... |
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