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17,400 | labels are oh-encoded when reading in the datawe are using one-hot-encoding to represent the labels (the actual digit drawne " "of the images one-hotencoding uses vector of binary values to represent numeric or categorical values as our labels are for the digits - the vector contains ten valuesone for each possible dig... |
17,401 | , now that we have our data importedit' time to think about the neural network step -defining the neural network architecture the architecture of the neural network refers to elements such as the number of layers in the networkthe number of units in each layerand how the units are connected between layers as neural net... |
17,402 | n_input input layer ( pixelsn_hidden st hidden layer n_hidden nd hidden layer n_hidden rd hidden layer n_output output layer ( - digitsthe following diagram shows visualization of the architecture we've designedwith each layer fully connected to the surrounding layersdiagram of neural network the term "deep neural netw... |
17,403 | can but it is often more computationally efficient to use smaller deep neural network to achieve the same task that would require shallow network with exponentially more hidden units shallow neural networks also often encounter overfittingwhere the network essentially memorizes the training data that it has seenand is ... |
17,404 | examples we are using at each step the dropout variable represents threshold at which we eliminate some units at random we will be using dropout in our final hidden layer to give each unit chance of being eliminated at every training step this helps prevent overfitting we have now defined the architecture of our neural... |
17,405 | possible classes the keep_prob tensor is used to control the dropout rateand we initialize it as placeholder rather than an immutable variable because we want to use the same tensor both for training (when dropout is set to and testing (when dropout is set to the parameters that the network will update in the training ... |
17,406 | for the biaswe use small constant value to ensure that the tensors activate in the intial stages and therefore contribute to the propagation the weights and bias tensors are stored in dictionary objects for ease of access add this code to your file to define the biasesmain py biases ' 'tf variable(tf constant( shape=[n... |
17,407 | layer' outputs and the current layer' weightsand add the bias to these values at the last hidden layerwe will apply dropout operation using our keep_prob value of the final step in building the graph is to define the loss function that we want to optimize popular choice of loss function in tensorflow programs is cross-... |
17,408 | we've now defined the network and built it out with tensorflow the next step is to feed data through the graph to train itand then test that it has actually learnt something step -training and testing the training process involves feeding the training dataset through the graph and optimizing the loss function every tim... |
17,409 | as list of booleans we can then cast this list to floats and calculate the mean to get total accuracy score we are now ready to initialize session for running the graph in this session we will feed the network with our training examplesand once trainedwe feed the same graph with new test examples to determine the accur... |
17,410 | network as model for testing our new data add this code to the filemain py train on mini batches for in range(n_iterations)batch_xbatch_y mnist train next_batch(batch_sizesess run(train_stepfeed_dict=xbatch_xybatch_ykeep_probdropout }print loss and accuracy (per minibatchif = minibatch_lossminibatch_accuracy sess run[c... |
17,411 | of images through the networkwe print out the loss and accuracy of that batch note that we should not be expecting decreasing loss and increasing accuracy hereas the values are per batchnot for the entire model we use mini-batches of images rather than feeding them through individually to speed up the training process ... |
17,412 | loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy iteration loss accuracy accuracy on test set to try and improve the accuracy of our modelor to learn more about th... |
17,413 | code to the top of the file to import two libraries necessary for image manipulation main py import numpy as np from pil import image then at the end of the fileadd the following line of code to load the test image of the handwritten digitmain py img np invert(image open("test_img png"convert(' ')ravel(the open functio... |
17,414 | add the following code to your file to test the image and print the outputted label main py prediction sess run(tf argmax(output_layer )feed_dict={ [img]}print ("prediction for test image:"np squeeze(prediction)th np squeeze function is called on the prediction to return the single integer from the array ( to go from [... |
17,415 | tensorflow websiteand see the research papers detailing the most accurate results on the mnist website now that you know how to build and train neural networkyou can try and use this implementation on your own dataor test it on other popular datasets such as the google streetview house numbersor the cifar- dataset for ... |
17,416 | how to build bot for atari with openai gym written by alvin wan edited by mark drake reinforcement learning is subfield within control theorywhich concerns controlling systems that change over time and broadly includes applications such as self-driving carsroboticsand bots for games throughout this guideyou will use re... |
17,417 | that govern one' choice of model complexity in machine learning prerequisites to complete this tutorialyou will needa server running ubuntu with at least gb of ram this server should have non-root user with sudo privileges configuredas well as firewall set up with ufw you can set this up by following this initial serve... |
17,418 | then create new virtual environment for the project you can name this virtual environment anything you' likeherewe will name it ataribotpython - venv ataribot activate your environmentsource ataribot/bin/activate on ubuntuas of version opencv requires few more packages be installed in order to function these include cm... |
17,419 | use pip to install the wheel packagethe reference implementation of the wheel packaging standard python librarythis package serves as an extension for building wheels and includes command line tool for working with whl filespython - pip install wheel in addition to wheelyou'll need to install the following packagesgyma... |
17,420 | thisuse pip once more to install gym' atari environmentswhich includes variety of atari video gamesincluding space invaderspython - pip install gym[atariif your installation of the gym[ataripackage was successfulyour output will end with the followingoutput installing collected packagesatari-pypillowpyopengl successful... |
17,421 | what you would see if you were playing the game using your preferred text editorcreate python file named bot_ _random py herewe'll use nanonano bot_ _random py notethroughout this guidethe botsnames are aligned with the step number in which they appearrather than the order in which they appear hencethis bot is named bo... |
17,422 | import gym import random def main()env gym make('spaceinvaders- 'env reset(nextadd an env step function this function can return the following kinds of valuesstatethe new state of the gameafter applying the provided action rewardthe increase in score that the state incurs by way of examplethis could be when bullet has ... |
17,423 | def main()env gym make('spaceinvaders- 'env reset(episode_reward while trueaction env action_space sample(_rewarddone_ env step(actionepisode_reward +reward if doneprint('reward%sepisode_rewardbreak finallyrun the main function include __name__ check to ensure main only runs when you invoke it directly with python bot_... |
17,424 | if **name*='**main**'main(save the file and exit the editor if you're using nanodo so by pressing ctrl+xythen enter thenrun your script by typingpython bot_ _random py your program will output numberakin to the following note that each time you run the file you will get different resultoutput making new envspaceinvader... |
17,425 | ""bot -make randombaseline agent for the spaceinvaders game ""import gym import random random seed( def main()env gym make('spaceinvaders- 'env seed( env reset(episode_reward while trueaction env action_space sample(_rewarddone_ env step(actionepisode_reward +reward if doneprint('reward%sepisode_rewardbreak if **name*=... |
17,426 | following in your terminalpython bot_ _random py this will output the following rewardexactlyoutput making new envspaceinvaders- reward this is your very first botalthough it' rather unintelligent since it doesn' account for the surrounding environment when it makes decisions for more reliable estimate of your bot' per... |
17,427 | right after env seed( )start new list of rewards/ataribot/bot_ _random py env seed( rewards [nest all code from env reset(to the end of main(in for loopiterating num_episodes times make sure to indent each line from env reset(to break by four spaces/ataribot/bot_ _random py def main()env gym make('spaceinvaders- 'env s... |
17,428 | the current episode' reward to the list of all rewards/ataribot/bot_ _random py if doneprint('reward%sepisode_rewardrewards append(episode_rewardbreak at the end of the main functionreport the average reward/ataribot/bot_ _random py def main()print('reward%sepisode_rewardbreak print('average reward (sum(rewardslen(rewa... |
17,429 | ""import gym import random random seed( make results reproducible num_episodes def main()env gym make('spaceinvaders- 'env seed( create the game make results reproducible rewards [for in range(num_episodes)env reset(episode_reward while trueaction env action_space sample(_rewarddone_ env step(actionrandom action episod... |
17,430 | main(save the fileexit the editorand run the scriptpython bot_ _random py this will print the following average rewardexactlyoutput making new envspaceinvaders- average reward we now have more reliable estimate of the baseline score to beat to create superior agentthoughyou will need to understand the framework for rei... |
17,431 | to build such functionwe will start with specific set of algorithms in reinforcement learning called -learning algorithms to illustrate theseconsider the initial state of gamewhich we'll call state your spaceship and the aliens are all in their starting positions thenassume we have access to magical " -tablewhich tells... |
17,432 | given particular stateit' easy for us to make decisionwe simply look at each possible action and its rewardthen take the action that corresponds with the highest expected reward reformulating the earlier policy more formallywe havepolicystate -argmax_{actionq(stateactionthis satisfies the requirements of decision-makin... |
17,433 | statethe state at current time step actionthe action taken at current time step rewardthe reward for current time step state'the new state for next time stepgiven that we took action action'all possible actions learning_ratethe learning rate discount_factorthe discount factorhow much reward "degradesas we propagate it ... |
17,434 | absolutely imperative that you navigate across the lake and retrieve the disc howeverthe ice is slipperyso you won' always move in the direction you intend the surface is described using grid like the followingsfff (sstarting pointsafefhfh (ffrozen surfacesafefffh (hholefall to your doomhffg (ggoalwhere the frisbee is ... |
17,435 | begin by updating the comment at the top of the file that describes the script' purpose because this is only commentthis change isn' necessary for the script to function properlybut it can be helpful for keeping track of what the script does/ataribot/bot_ _q_table py ""bot -build simple -learning agent for frozenlake "... |
17,436 | /ataribot/bot_ _q_table py import random random seed( make results reproducible np random seed( nextmake the game states accessible update the env reset(line to say the followingwhich stores the initial state of the game in the variable state/ataribot/bot_ _q_table py for \ in range(num_episodes)state env reset(update ... |
17,437 | episode_reward +rewardadd line updating the variable state this keeps the variable state updated for the next iterationas you will expect state to reflect the current state/ataribot/bot_ _q_table py while trueepisode_reward +reward state state if donein the if done blockdelete the print statement which prints the rewar... |
17,438 | for in range(num_episodes)state env reset(episode_reward while trueaction env action_space sample(state rewarddone_ env step(actionepisode_reward +reward state state if donerewards append(episode_reward)break nextadd the ability for the agent to trade off between exploration and exploitation right before your main game... |
17,439 | np zeros((env observation_space nenv action_space )for episode in range( num_episodes )inside the while trueinner game loopcreate noise noiseor meaninglessrandom datais sometimes introduced when training deep neural networks because it can improve both the performance and the accuracy of the model note that the higher ... |
17,440 | noise np random random(( env action_space )(episode** action np argmax( [state:noisestate rewarddone_ env step(actionyour main game loop will then match the following/ataribot/bot_ _q_table py np zeros((env observation_space nenv action_space )for episode in range( num_episodes )state env reset(episode_reward while tru... |
17,441 | equationan equation widely used in machine learning to find the optimal policy within given environment the bellman equation incorporates two ideas that are highly relevant to this project firsttaking particular action from particular state many times will result in good estimate for the -value associated with that sta... |
17,442 | discount_factor learning_rate compute the new target -valueright after the line containing env step)/ataribot/bot_ _q_table py state rewarddone_ env step(actionqtarget reward discount_factor np max( [state :]episode_reward +reward on the line directly after qtargetupdate the -value table using weighted average of the o... |
17,443 | np zeros((env observation_space nenv action_space )for episode in range( num_episodes )state env reset(episode_reward while truenoise np random random(( env action_space )(episode** action np argmax( [state:noisestate rewarddone_ env step(actionqtarget reward discount_factor np max( [state :] [stateaction -learning_rat... |
17,444 | from typing import list import gym right after learning_rate outside of the main functiondeclare the interval and format for reports/ataribot/bot_ _q_table py learning_rate report_interval report ' -ep average best -ep average average '(episode % )def main()before the main functionadd new function that will populate th... |
17,445 | """print rewards report for current episode average for last episodes best -episode average across all time average for all episodes across time ""print(report np mean(rewards[- :])max([np mean(rewards[ : + ]for in range(len(rewards )])np mean(rewards)episode)def main()change the game to frozenlake instead of spaceinva... |
17,446 | if donerewards append(episode_rewardif episode report_interval = print_report(rewardsepisodeat the end of the main(functionreport both averages once more do this by replacing the line that reads print('average reward (sum(rewardslen(rewards))with the following highlighted line/ataribot/bot_ _q_table py def main()break ... |
17,447 | import gym import numpy as np import random random seed( make results reproducible np random seed( make results reproducible num_episodes discount_factor learning_rate report_interval report ' -ep average best -ep average average '(episode % )def print_report(rewardslistepisodeint)"""print rewards report for current ep... |
17,448 | episode)def main()env gym make('frozenlake- 'env seed( create the game make results reproducible rewards [ np zeros((env observation_space nenv action_space )for episode in range( num_episodes )state env reset(episode_reward while truenoise np random random(( env action_space )(episode** action np argmax( [state:noises... |
17,449 | print_report(rewards- if __name__ ='__main__'main(save the fileexit your editorand run the scriptpython bot_ _q_table py your output will match the followingoutput -ep average best -ep average average (episode -ep average best -ep average average (episode -ep average best -ep average average (episode -ep average best -... |
17,450 | -ep average best -ep average average (episode - you now have your first non-trivial bot for gamesbut let' put this average reward of into perspective according to the gym frozenlake page"solvingthe game means attaining -episode average of informally"solvingmeans "plays the game very wellwhile not in record timethe -tab... |
17,451 | abstractions and you'll use neural network to approximate your -function howeveryour neural network will be extremely simpleyour output (sis matrix multiplied by your input this is known as neural network with one fully-connected layerq(sws to reiteratethe goal is to reimplement all of the logic from the bots we've alr... |
17,452 | right below import random additionallyadd tf set_radon_seed( right below np random seed( this will ensure that the results of this script will be repeatable across all sessions/ataribot/bot_ _q_network py import random import tensorflow as tf random seed( np random seed( tf set_random_seed( redefine your hyperparameter... |
17,453 | '(episode % )nextyou will add one-hot encoding function in shortone-hot encoding is process through which variables are converted into form that helps machine learning algorithms make better predictions if you' like to learn more about one-hot encodingyou can check out adversarial examples in computer visionhow to buil... |
17,454 | abstractions before doing thatthoughyou'll need to first create placeholders for your data in your main functiondirectly beneath rewards=[]insert the following highlighted content hereyou define placeholders for your observation at time (as obs_t_phand time + (as obs_tp _ph)as well as placeholders for your actionreward... |
17,455 | following highlighted lines this code starts your computation by computing (safor all to make q_current and ( ' 'for all ato make q_target/ataribot/bot_ _q_network py rew_ph tf placeholder(shape=()dtype=tf float q_target_ph tf placeholder(shape=[ n_actions]dtype=tf float setup computation graph tf variable(tf random_un... |
17,456 | q_target tf matmul(obs_tp _phwq_target_max tf reduce_max(q_target_phaxis= q_target_sa rew_ph discount_factor q_target_max q_current_sa q_current[ act_pherror tf reduce_sum(tf square(q_target_sa q_current_sa)pred_act_ph tf argmax(q_current np zeros((env observation_space nenv action_space )for episode in range( num_epis... |
17,457 | nextset up the body of the game loop to do thispass data to the tensorflow placeholders and tensorflow' abstractions will handle the computation on the gpureturning the result of the algorithm start by deleting the old -table and logic specificallydelete the lines that define (right before the for loop)noise (in the wh... |
17,458 | directly above the for loopadd the following two highlighted lines these lines initialize tensorflow session which in turn manages the resources needed to run operations on the gpu the second line initializes all the variables in your computation graphfor exampleinitializing weights to before updating them additionally... |
17,459 | while true take step using best action or random action obs_t_oh one_hot(obs_tn_obsaction session run(pred_act_phfeed_dict={obs_t_phobs_t_oh})[ if np random rand( exploration_probability(episode)action env action_space sample(after the line containing env step(action)insert the following to train the neural network in ... |
17,460 | episode_reward +reward your final file will match this source code/ataribot/bot_ _q_network py ""bot -use -learning network to train bot ""from typing import list import gym import numpy as np import random import tensorflow as tf random seed( np random seed( tf set_random_seed( num_episodes discount_factor learning_ra... |
17,461 | def one_hot(iintnint-np array"""implements one-hot encoding by selecting the ith standard basis vector""return np identity( )[ireshape(( - )def print_report(rewardslistepisodeint)"""print rewards report for current episode average for last episodes best -episode average across all time average for all episodes across t... |
17,462 | n_obsn_actions env observation_space nenv action_space obs_t_ph tf placeholder(shape=[ n_obs]dtype=tf float obs_tp _ph tf placeholder(shape=[ n_obs]dtype=tf float act_ph tf placeholder(tf int shape=()rew_ph tf placeholder(shape=()dtype=tf float q_target_ph tf placeholder(shape=[ n_actions]dtype=tf float setup computati... |
17,463 | episode_reward while true take step using best action or random action obs_t_oh one_hot(obs_tn_obsaction session run(pred_act_phfeed_dict={obs_t_phobs_t_oh})[ if np random rand( exploration_probability(episode)action env action_space sample(obs_tp rewarddone_ env step(action train model obs_tp _oh one_hot(obs_tp n_obsq... |
17,464 | print_report(rewardsepisodebreak print_report(rewards- if __name__ ='__main__'main(save the fileexit your editorand run the scriptpython bot_ _q_network py your output will end with the followingexactlyoutput -ep average best -ep average average (episode -ep average best -ep average average (episode -ep average best -e... |
17,465 | -ep average best -ep average average (episode -ep average best -ep average average (episode - you've now trained your very first deep -learning agent for game as simple as frozenlakeyour deep -learning agent required episodes to train imagine if the game were far more complex how many training samples would that requir... |
17,466 | train case in pointyour neural network-based -learning agent required episodes to solve frozenlake adding second layer to the neural network agent quadruples the number of necessary training episodes with increasingly complex neural networksthis divide only grows to maintain the same error rateincreasing model complexi... |
17,467 | step -building least squares agent for frozen lake the least squares methodalso known as linear regressionis means of regression analysis used widely in the fields of mathematics and data science in machine learningit' often used to find the optimal linear model of two parameters or datasets in step you built neural ne... |
17,468 | nano bot_ _ls py againupdate the comment at the top of the file describing what this script will do/ataribot/bot_ _q_network py ""bot -build least squares -learning agent for frozenlake ""before the block of imports near the top of your fileadd two more imports for type checking/ataribot/bot_ _ls py from typing import ... |
17,469 | larger valuesthe agent will be able to issue stronger performance/ataribot/bot_ _ls py num_episodes discount_factor learning_rate w_lr report_interval before your print_report functionadd the following higher-order function it returns lambda -an anonymous function -that abstracts away the model/ataribot/bot_ _ls py rep... |
17,470 | after makeqadd another functioninitializewhich initializes the model using normally-distributed values/ataribot/bot_ _ls py def makeq(modelnp array-callable[[np array]np array]"""returns -functionwhich takes state -distribution over actions""return lambda xx dot(modeldef initialize(shapetuple)"""initialize model"" np r... |
17,471 | return wq def train(xnp arrayynp arraywnp array-tuple[np arraycallable]"""train the modelusing solution to ridge regression"" np eye( shape[ ]neww np linalg inv( dot( - idot( dot( ) w_lr neww ( w_lr\ makeq(wreturn wq def print_report(rewardslistepisodeint)after trainadd one last functionone_hotto perform one-hot encodi... |
17,472 | return np identity( )[idef print_report(rewardslistepisodeint)following thisyou will need to modify the training logic in the previous script you wrotethe -table was updated every iteration this scripthoweverwill collect samples and labels every time step and train new model every steps additionallyinstead of holding -... |
17,473 | /ataribot/bot_ _ls py def main()for episode in range( num_episodes )if len(states> stateslabels [][modify the line directly after this onewhich defines state env reset()so that it becomes the following this will one-hot encode the state immediatelyas all of its usages will require one-hot vector/ataribot/bot_ _ls py fo... |
17,474 | for episode in range( num_episodes )episode_reward while truestates append(statenoise np random random(( env action_space )(episode\*\* update the computation for actiondecrease the probability of noiseand modify the -function evaluation/ataribot/bot_ _ls py while truestates append(statenoise np random random(( *action... |
17,475 | state one_hot(state n_obsqtarget reward discount_factor np max( (state )delete the line that updates [state,actionand replace it with the following lines this code takes the output of the current model and updates only the value in this output that corresponds to the current action taken as resultq-values for the other... |
17,476 | if len(states = wq train(np array(states)np array(labels)wif doneensure that your code matches the following/ataribot_ _ls py ""bot -build least squares -learning agent for frozenlake ""from typing import tuple from typing import callable from typing import list import gym import numpy as np import random random seed( ... |
17,477 | '(episode % )def makeq(modelnp array-callable[[np array]np array]"""returns -functionwhich takes state -distribution over actions""return lambda xx dot(modeldef initialize(shapetuple)"""initialize model"" np random normal( shapeq makeq(wreturn wq def train(xnp arrayynp arraywnp array-tuple[np arraycallable]"""train the... |
17,478 | """implements one-hot encoding by selecting the ith standard basis vector""return np identity( )[idef print_report(rewardslistepisodeint)"""print rewards report for current episode average for last episodes best -episode average across all time average for all episodes across time ""print(report np mean(rewards[- :])ma... |
17,479 | if len(states> stateslabels [][state one_hot(env reset()n_obsepisode_reward while truestates append(statenoise np random random(( n_actions)episode action np argmax( (statenoisestate rewarddone_ env step(actionstate one_hot(state n_obsqtarget reward discount_factor np max( (state )label (statelabel[action( learning_rat... |
17,480 | main(thensave the fileexit the editorand run the scriptpython bot_ _ls py this will output the followingoutput -ep average best -ep average average (episode -ep average best -ep average average (episode -ep average best -ep average average (episode -ep average best -ep average average (episode -ep average best -ep aver... |
17,481 | -ep average best -ep average average (episode -ep average best -ep average average (episode - recall thataccording to the gym frozenlake page"solvingthe game means attaining -episode average of here the agent acheives an average of meaning it was able to solve the game in episodes although this does not solve the game ... |
17,482 | without any human intervention was developed by the researchers at deepmindwho also trained their agent to play variety of atari games deepmind' original deep -learning (dqnpaper recognized two important issues correlated statestake the state of our game at time which we will call say we update ( )according to the rule... |
17,483 | those states the team at deepmind duplicated (saone is called q_current(sa)which is the -function you update you need another -function for successor statesq_target( ' ')which you won' update recall q_target( ' 'is used to generate your labels by separating q_current from q_target and fixing the latteryou fix the distr... |
17,484 | the input consists of four statesstacked we will address these constraints in more detail later on for nowdownload the script by typingwget you will now run this pretrained space invaders agent to see how it performs unlike the past few bots we've usedyou will write this script from scratch create new script filenano b... |
17,485 | from bot_ _a import c_model def main()if **name*='**main**'main(directly after your importsset random seeds to make your results reproducible alsodefine hyperparameter num_episodes which will tell the script how many episodes to run the agent for/ataribot/bot_ _dqn py import tensorflow as tf from bot_ _a import c_model... |
17,486 | num_episodes def downsample(state)return cv resize(state( )interpolation=cv inter_linear)[nonedef main()create the game environment at the start of your main function and seed the environment so that the results are reproducible/ataribot/bot_ _dqn py def main()env gym make('spaceinvaders- 'create the game env seed( mak... |
17,487 | initialize the pretrained model with the pretrained model parameters that you downloaded at the beginning of this step/ataribot/bot_ _dqn py def main()env gym make('spaceinvaders- 'create the game env seed( make results reproducible rewards [model c_model(load='models/spaceinvaders- tfmodel'nextadd some lines telling t... |
17,488 | for in range(num_episodes)episode_reward states [downsample(env reset())while trueinstead of accepting one state at timethe new neural network accepts four states at time as resultyou must wait until the list of states contains at least four states before applying the pretrained model add the following lines below the ... |
17,489 | author no part of this publication may be reproducedstored in retrieval systemor transmitted in any form or by any meanselectronicmechanicalphotocopyingrecording and/or otherwise without the prior written permission of the author and the publisher first edition published by mrs meena pandey for himalaya publishing hous... |
17,490 | to my parents |
17,491 | having spent more than years in the field of information technology and having published more than research papersi feel obliged to share my knowledgeexperienceanalysis and results of real-life situations the book has evolved from my teaching experience in several technical institutions and rich experience of working i... |
17,492 | expression of feelings by words loses its significance when it comes to say few words of gratitudeyet to express it in some formhowever imperfectis duty towards those who helped offer my special gratitude to the almighty god for his blessings that has made completion of this book possible find myself with paucity of wo... |
17,493 | section icore programming in python introduction to python features of python installation of python first interaction command line versus scripts comments identifiers reserved words whitespace input and output in python operators arithmetic operators assignment operators relational operators logical operators boolean ... |
17,494 | function without arguments function with arguments nesting of functions recursive function scope of variables within functions data structures lists creating list accessing list elements functions for list tuples creating tuple accessing tuple elements functions for tuple dictionary creating dictionary accessing dictio... |
17,495 | mathematical operators for single and multiple multidimensional arrays data processing with pandas basics of data frame creating data frame adding rows and columns to the data frame deleting rows and columns from the data frame import of data basic functions of data frame relational and logical operators for filtering ... |
17,496 | stripping whitespace functions data type check functions partition functions other functions regular expressions from "remodule text pre-processing using string module the nltk toolkit shallow parsing displaying similar words creating summary of the document by data cleaning section iiicore statistics in python basic s... |
17,497 | section icore programming in python introduction to python python is an interpreted high-levelgeneral-purpose programming language created by guido van rossum and first released in python is van rossum' vision of small core language with large standard library and easily extensible interpreterthat stemmed from his frus... |
17,498 | python language is more expressive it means that it is more understandable and readable python is dynamically-typed this means that the type for value is decided at runtimenot in advance this is why we don' need to specify the type of data while declaring it python is very easy to code compared to other popular languag... |
17,499 | first interaction in python environment setuplaunch python interpreter to get prompt "we will start learning python programming by writing "helloprogram depending on the needswe can program either at python command prompt or jupyter (command lineor we can write python script file in spyder or jupyter or pycharm in all ... |
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