Programming language translators

 

Assembler


compiler



interpreter 







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Audio Book using python

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we can convert any pdf to an audiobook using python. Only two Python libraries are required. 1. pyttsx3 2. PyPDF2 and you need to import your pdf document under this project folder too. let's start 💻 step 1:  import both libraries  using import keyword, import pyttsx3 import PyPDF2 💻 step 2: we need to open our pdf document using the  open function. open syntax book  = open(“ filename ”, “mode”) where book is the variable to add the file object. filename is your pdf file name. book = open ( 'event.pdf' , 'rb' ) 💻 step 3: call pdf reader function to read your pdf document. pdfReader = PyPDF2.PdfFileReader(book) 💻 step 4: Get pdf pages details for example 200 page pdf or 1000 page pdf document print(page) command is just optional. page = pdfReader.numPages print (page)// optional 💻 step 5: initialize speaker and save it into speaker   variable  speaker = pyttsx3.init() 💻 step 6: define which page you gonna read, using the get page function...
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AttributeError : module 'tensorflow' has no attribute 'global_variables_initializer'

module 'tensorflow' has no attribute 'global_variables_initializer'. it is an AttributeError. 💻problem #init_op = tf.initialize_all_variables() init_op =  tf.global_variables_initializer () --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-13-9cab3748941a> in <module>() 12 13 #init_op = tf.initialize_all_variables() ---> 14 init_op = tf.global_variables_initializer() 15 16 sess = tf.Session() AttributeError: module 'tensorflow' has no attribute 'global_variables_initializer' 💻Changes should be made #init_op = tf.initialize_all_variables() init = tf.compat.v1.global_variables_initializer ()
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TensorFlow Train/test /evaluation flow

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 TensorFlow  Train/test /evaluation flow step1: Choose a model First, we'll need to choose which machine learning algorithm we want to use to build our model. we can pick any standard machine learning algorithm but in this section, we will be using neural networks. then we can start with the training phase. step2: we train the algorithm by showing its training data and the expected output for the data and it has to figure out how to come up with the expected result. in other words, the algorithm learns how to transform the input to produce the correct output. for example, we can train it to do multiplication by showing it two numbers and the expected result and it will eventually work out that we want to multiply the two numbers. After we train the model, we will load up the second set of data, which has never seen before called the testing data set. This is the test phase, we will feed this testing data through the model and make sure it is able to predict the correct result ...
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