{"id":26185,"date":"2023-06-08T13:53:23","date_gmt":"2023-06-08T10:23:23","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/the-art-of-machine-intelligence-how-to-train-your-python-to-think-55f8\/"},"modified":"2023-06-08T13:53:23","modified_gmt":"2023-06-08T10:23:23","slug":"the-art-of-machine-intelligence-how-to-train-your-python-to-think-55f8","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/the-art-of-machine-intelligence-how-to-train-your-python-to-think-55f8\/","title":{"rendered":"\u0647\u0646\u0631 \u0647\u0648\u0634 \u0645\u0627\u0634\u06cc\u0646\u06cc: \u0686\u06af\u0648\u0646\u0647 \u067e\u0627\u06cc\u062a\u0648\u0646 \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0641\u06a9\u0631 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645"},"content":{"rendered":"
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\u0633\u0644\u0628 \u0645\u0633\u0626\u0648\u0644\u06cc\u062a: \u0647\u06cc\u0686 \u0686\u0648\u0628 \u062c\u0627\u062f\u0648\u06cc\u06cc \u06cc\u0627 \u062f\u06cc\u06af \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0647\u0648\u0634 \u0645\u0627\u0634\u06cc\u0646\u06cc \u0644\u0627\u0632\u0645 \u0646\u06cc\u0633\u062a. \u062c\u0627\u062f\u0648 \u0628\u06cc\u0634\u062a\u0631 \u0628\u06cc\u0646 \u06a9\u06cc\u0628\u0648\u0631\u062f \u0648 \u0635\u0646\u062f\u0644\u06cc \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f.<\/em><\/p>\n

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import<\/span> tensorflow<\/span> as<\/span> tf<\/span>\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\n\nprint<\/span>(<\/span>\"Python, are you awake?\"<\/span>)<\/span>\n\n# Feed the sleepy python with some training data\n<\/span>data<\/span> =<\/span> np<\/span>.<\/span>array<\/span>([[<\/span>1<\/span>,<\/span> 2<\/span>],<\/span> [<\/span>2<\/span>,<\/span> 3<\/span>],<\/span> [<\/span>3<\/span>,<\/span> 4<\/span>],<\/span> [<\/span>4<\/span>,<\/span> 5<\/span>]])<\/span>\nlabels<\/span> =<\/span> np<\/span>.<\/span>array<\/span>([<\/span>3<\/span>,<\/span> 5<\/span>,<\/span> 7<\/span>,<\/span> 9<\/span>])<\/span>\n\n# Craft a magic spell (I mean, model) using TensorFlow\n<\/span>model<\/span> =<\/span> tf<\/span>.<\/span>keras<\/span>.<\/span>Sequential<\/span>([<\/span>tf<\/span>.<\/span>keras<\/span>.<\/span>layers<\/span>.<\/span>Dense<\/span>(<\/span>units<\/span>=<\/span>1<\/span>,<\/span> input_shape<\/span>=<\/span>[<\/span>2<\/span>])])<\/span>\nmodel<\/span>.<\/span>compile<\/span>(<\/span>optimizer<\/span>=<\/span>'sgd'<\/span>,<\/span> loss<\/span>=<\/span>'mean_squared_error'<\/span>)<\/span>\n\n# Whisper the sacred incantations (Train the model)\n<\/span>model<\/span>.<\/span>fit<\/span>(<\/span>data<\/span>,<\/span> labels<\/span>,<\/span> epochs<\/span>=<\/span>500<\/span>)<\/span>\n\n# Speak Parseltongue to check if the serpent is thinking\n<\/span>prediction<\/span> =<\/span> model<\/span>.<\/span>predict<\/span>(<\/span>np<\/span>.<\/span>array<\/span>([[<\/span>5<\/span>,<\/span> 6<\/span>]]))<\/span>\nprint<\/span>(<\/span>f<\/span>\"Prediction from the all-knowing serpent: <\/span>{<\/span>prediction<\/span>}<\/span>\"<\/span>)<\/span>\n<\/code><\/pre>\n
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from<\/span> sklearn<\/span> import<\/span> tree<\/span>\n\n# Teach Python the mystical language of fruit\n# features: [weight (grams), texture (1:smooth, 0:bumpy)]\n<\/span>features<\/span> =<\/span> [[<\/span>140<\/span>,<\/span> 1<\/span>],<\/span> [<\/span>130<\/span>,<\/span> 1<\/span>],<\/span> [<\/span>150<\/span>,<\/span> 0<\/span>],<\/span> [<\/span>170<\/span>,<\/span> 0<\/span>]]<\/span>\nlabels<\/span> =<\/span> [<\/span>\"apple\"<\/span>,<\/span> \"apple\"<\/span>,<\/span> \"orange\"<\/span>,<\/span> \"orange\"<\/span>]<\/span>\n\n# The Book of Trees\n<\/span>clf<\/span> =<\/span> tree<\/span>.<\/span>DecisionTreeClassifier<\/span>()<\/span>\nclf<\/span> =<\/span> clf<\/span>.<\/span>fit<\/span>(<\/span>features<\/span>,<\/span> labels<\/span>)<\/span>\n\n# Make Python use its mystical powers\n<\/span>print<\/span>(<\/span>\"Wise Python says:\"<\/span>,<\/span> clf<\/span>.<\/span>predict<\/span>([[<\/span>160<\/span>,<\/span> 0<\/span>]]))<\/span>\n<\/code><\/pre>\n
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\u0622\u0646 \u0631\u0627 \u0628\u0647 \u062e\u0648\u0628\u06cc (\u0628\u0627 \u062f\u0627\u062f\u0647) \u062a\u063a\u0630\u06cc\u0647 \u06a9\u0646\u06cc\u062f\u060c \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0645\u0627 \u0647\u0648\u0634\u0645\u0646\u062f \u0631\u0634\u062f \u062e\u0648\u0627\u0647\u062f \u06a9\u0631\u062f.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062a\u063a\u0630\u06cc\u0647 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0645\u0646\u062c\u0631 \u0628\u0647 \u06cc\u06a9 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0686\u0627\u0642 \u0634\u0648\u062f – \u0648 \u0628\u0647 \u0645\u0646 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0646\u06cc\u062f\u060c \u0634\u0645\u0627 \u06cc\u06a9 \u0645\u0627\u0631 \u0686\u0627\u0642 \u0646\u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f.  \u0645\u062a\u0639\u0627\u062f\u0644 \u06a9\u0631\u062f\u0646 \u0631\u0698\u06cc\u0645 \u063a\u0630\u0627\u06cc\u06cc \u0648 \u062d\u0641\u0638 \u0634\u06a9\u0644 \u067e\u0627\u06cc\u062a\u0648\u0646 \u062e\u0648\u062f \u06cc\u06a9 \u0647\u0646\u0631 \u0627\u0633\u062a.<\/p>\n
\n
from<\/span> sklearn.model_selection<\/span> import<\/span> train_test_split<\/span>\nfrom<\/span> sklearn<\/span> import<\/span> datasets<\/span>\n\n# Fetch the sacred iris dataset\n<\/span>iris<\/span> =<\/span> datasets<\/span>.<\/span>load_iris<\/span>()<\/span>\n\n# Split the dataset into a training and testing set\n<\/span>X_train<\/span>,<\/span> X_test<\/span>,<\/span> y_train<\/span>,<\/span> y_test<\/span> =<\/span> train_test_split<\/span>(<\/span>iris<\/span>.<\/span>data<\/span>,<\/span> iris<\/span>.<\/span>target<\/span>,<\/span> test_size<\/span>=<\/span>0.2<\/span>)<\/span>\n\n# Train the Python but don\u2019t overfeed\n<\/span>model<\/span>.<\/span>fit<\/span>(<\/span>X_train<\/span>,<\/span> y_train<\/span>,<\/span> epochs<\/span>=<\/span>100<\/span>,<\/span> validation_data<\/span>=<\/span>(<\/span>X_test<\/span>,<\/span> y_test<\/span>))<\/span>\n<\/code><\/pre>\n
\n
\n    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
\u0628\u0647 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f: \u0645\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u06cc\u06a9 \u0645\u0627\u0634\u06cc\u0646 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0636\u0639\u06cc\u0641 \u0648 \u0636\u0639\u06cc\u0641.  \u0627\u0632 \u062a\u0637\u0628\u06cc\u0642 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u062e\u0648\u062f\u062f\u0627\u0631\u06cc \u06a9\u0646\u06cc\u062f\u060c \u06a9\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc\u200c\u0627\u0641\u062a\u062f \u06a9\u0647 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0645\u0627 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0631\u0627 \u0628\u0647 \u062c\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0627\u0632 \u0622\u0646\u200c\u0647\u0627 \u062d\u0641\u0638 \u0645\u06cc\u200c\u06a9\u0646\u062f.  \u0634\u0645\u0627 \u0646\u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 \u062d\u0642\u0627\u06cc\u0642 \u0631\u0627 \u0645\u0627\u0646\u0646\u062f \u06cc\u06a9 \u0637\u0648\u0637\u06cc \u0628\u0627\u0632\u06af\u0648 \u06a9\u0646\u062f!<\/p>\n
\u0633\u0644\u0628 \u0645\u0633\u0626\u0648\u0644\u06cc\u062a: \u062a\u0646\u0627\u0633\u0628 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0645\u0646\u062c\u0631 \u0628\u0647 \u0634\u06a9\u0645 \u062f\u0631\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0648\u062f.  \u0628\u0631\u0627\u06cc \u0631\u0698\u06cc\u0645 \u063a\u0630\u0627\u06cc\u06cc \u0645\u0646\u0627\u0633\u0628 \u0647\u0645\u06cc\u0634\u0647 \u0628\u0627 \u06cc\u06a9 \u0645\u062a\u062e\u0635\u0635 \u062f\u0627\u062f\u0647 \u0645\u0634\u0648\u0631\u062a \u06a9\u0646\u06cc\u062f.<\/em><\/p>\n
\ud83d\udd39 \u0645\u0646\u062d\u0631\u0641 \u06a9\u0631\u062f\u0646 \u062d\u0648\u0627\u0633 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 \u0627\u0633\u0628\u0627\u0628 \u0628\u0627\u0632\u06cc \u0647\u0627 (\u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627 \u0648 \u0686\u0627\u0631\u0686\u0648\u0628 \u0647\u0627)<\/strong><\/p>\n
\u062f\u0631\u06af\u06cc\u0631 \u0646\u06af\u0647 \u062f\u0627\u0634\u062a\u0646 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0633\u06cc\u0627\u0631 \u0645\u0647\u0645 \u0627\u0633\u062a.  \u0628\u062f\u0648\u0646 \u0627\u0633\u0628\u0627\u0628 \u0628\u0627\u0632\u06cc \u0622\u0646\u060c \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0633\u0631\u06a9\u0634 \u0634\u0648\u062f.  \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u063a\u0646\u06cc \u0627\u0632 \u0627\u0633\u0628\u0627\u0628\u200c\u0628\u0627\u0632\u06cc\u200c\u0647\u0627 \u0631\u0627 \u0628\u0647 \u067e\u0627\u06cc\u062a\u0648\u0646 \u062e\u0648\u062f \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u062f\u0647\u06cc\u062f\u060c \u0645\u0627\u0646\u0646\u062f TensorFlow \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u060c OpenCV \u0628\u0631\u0627\u06cc \u0628\u06cc\u0646\u0627\u06cc\u06cc \u0631\u0627\u06cc\u0627\u0646\u0647\u060c \u06cc\u0627 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0631\u0627\u06cc \u067e\u0631 \u06a9\u0631\u062f\u0646 \u062f\u0627\u062f\u0647\u200c\u0647\u0627.<\/p>\n
\n
# Here\u2019s a simple plaything using Pandas\n<\/span>import<\/span> pandas<\/span> as<\/span> pd<\/span>\n\n# Create a toy dataset\n<\/span>data<\/span> =<\/span> {<\/span>'col1'<\/span>:<\/span> [<\/span>1<\/span>,<\/span> 2<\/span>],<\/span> 'col2'<\/span>:<\/span> [<\/span>3<\/span>,<\/span> 4<\/span>]}<\/span>\ndf<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>(<\/span>data<\/span>)<\/span>\n\n# Let Python play with the data\n<\/span>print<\/span>(<\/span>\"Python is playing...<\/span>\\n<\/span>\"<\/span>,<\/span> df<\/span>)<\/span>\n<\/code><\/pre>\n
\n
\n    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
\u0628\u0647 \u0627\u06cc\u0646 \u062a\u0631\u062a\u06cc\u0628\u060c \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0645\u0627 \u062e\u0648\u0634\u062d\u0627\u0644\u060c \u062f\u0631\u06af\u06cc\u0631 \u0645\u06cc \u0634\u0648\u062f \u0648 \u062a\u0633\u0644\u0637 \u0628\u0631 \u062c\u0647\u0627\u0646 \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u0646\u0645\u06cc \u06a9\u0646\u062f.  \u06cc\u06a9 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0627\u062f \u06cc\u06a9 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0647\u0648\u0634\u0645\u0646\u062f \u0627\u0633\u062a.<\/p>\n
\ud83d\udd39 \u0632\u0645\u0632\u0645\u0647 \u067e\u0627\u06cc\u062a\u0648\u0646 \ud83d\udde3\ufe0f<\/strong><\/p>\n
\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0631\u0642\u0631\u0627\u0631\u06cc \u0627\u0631\u062a\u0628\u0627\u0637 \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0636\u0631\u0648\u0631\u06cc \u0627\u0633\u062a.  \u06af\u0627\u0647\u06cc \u0627\u0648\u0642\u0627\u062a\u060c \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0645\u0627 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u0627\u0639\u062b \u0639\u0635\u0628\u0627\u0646\u06cc\u062a (\u06cc\u0627 \u062e\u0637\u0627) \u0634\u0648\u062f.  \u06cc\u06a9 \u0646\u062c\u0648\u0627\u06a9\u0646\u0646\u062f\u0647 \u0648\u0627\u0642\u0639\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0627 \u0627\u0634\u06a9\u0627\u0644 \u0632\u062f\u0627\u06cc\u06cc \u0645\u0644\u0627\u06cc\u0645 \u0648 \u06a9\u0644\u0645\u0627\u062a \u0645\u0647\u0631\u0628\u0627\u0646\u0627\u0646\u0647 \u062c\u0627\u0646\u0648\u0631 \u0631\u0627 \u0622\u0631\u0627\u0645 \u06a9\u0646\u062f.<\/p>\n
\n
try<\/span>:<\/span>\n    # Something risky\n<\/span>    x<\/span> =<\/span> 1<\/span> \/<\/span> 0<\/span>\nexcept<\/span> ZeroDivisionError<\/span> as<\/span> e<\/span>:<\/span>\n    print<\/span>(<\/span>\"Shhh, it\u2019s okay Python. You can\u2019t divide by zero, silly snake.\"<\/span>)<\/span>\n<\/code><\/pre>\n
\n
\n    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
\u0633\u0644\u0628 \u0645\u0633\u0626\u0648\u0644\u06cc\u062a: \u0647\u06cc\u0686 \u0632\u0645\u0632\u0645\u0647 \u0648\u0627\u0642\u0639\u06cc \u062f\u0631\u06af\u06cc\u0631 \u0646\u06cc\u0633\u062a.  \u0627\u06af\u0631 \u062f\u0631 \u062d\u0627\u0644 \u0632\u0645\u0632\u0645\u0647 \u06a9\u0631\u062f\u0646 \u0686\u06cc\u0632\u0647\u0627\u06cc \u0634\u06cc\u0631\u06cc\u0646 \u0628\u0627 \u06a9\u062f \u062e\u0648\u062f \u06af\u0631\u0641\u062a\u0627\u0631 \u0634\u0648\u06cc\u062f\u060c \u0645\u0631\u062f\u0645 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0639\u0642\u0644 \u0634\u0645\u0627 \u0631\u0627 \u0632\u06cc\u0631 \u0633\u0648\u0627\u0644 \u0628\u0628\u0631\u0646\u062f.<\/em><\/p>\n
\ud83d\udd39 \u0648\u06cc\u062a\u0631\u06cc\u0646 \u06af\u0631\u0646\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 \ud83c\udfaa<\/strong><\/p>\n
\u0627\u06a9\u0646\u0648\u0646 \u06a9\u0647 \u067e\u0627\u06cc\u062a\u0648\u0646 \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u062e\u0648\u0628\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0627\u06cc\u062f\u060c \u0648\u0642\u062a \u0622\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0646\u0645\u0627\u06cc\u0634\u06cc \u0631\u0627 \u0628\u0647 \u0646\u0645\u0627\u06cc\u0634 \u0628\u06af\u0630\u0627\u0631\u06cc\u062f.  \u0628\u0627 \u0645\u0642\u0627\u0628\u0644\u0647 \u0628\u0627 \u0645\u0634\u06a9\u0644\u0627\u062a \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc\u060c \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0642\u06cc\u0645\u062a \u0633\u0647\u0627\u0645\u060c \u06cc\u0627 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u06af\u0631\u0628\u0647\u200c\u0647\u0627 \u0628\u0627 \u062f\u0642\u062a \u0628\u06cc\u200c\u0646\u0638\u06cc\u0631\u060c \u0645\u0647\u0627\u0631\u062a \u062e\u0648\u062f \u0631\u0627 \u0646\u0634\u0627\u0646 \u062f\u0647\u06cc\u062f.  \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0645\u0627 \u062f\u0631 \u06a9\u0627\u0646\u0648\u0646 \u062a\u0648\u062c\u0647 \u0642\u0631\u0627\u0631 \u0628\u06af\u06cc\u0631\u062f\u060c \u0627\u0645\u0627 \u0628\u0647 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f\u060c \u0628\u0627 \u0642\u062f\u0631\u062a \u0632\u06cc\u0627\u062f\u060c \u0645\u0633\u0626\u0648\u0644\u06cc\u062a \u0628\u0632\u0631\u06af\u06cc \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 \u062f\u0627\u0631\u062f. <\/p>\n
\u0633\u0644\u0628 \u0645\u0633\u0626\u0648\u0644\u06cc\u062a: \u067e\u0627\u06cc\u062a\u0648\u0646 \u062e\u0648\u062f \u0631\u0627 \u0628\u0633\u062a\u0647 \u0646\u06af\u0647 \u062f\u0627\u0631\u06cc\u062f.  \u06cc\u06a9 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0631\u0647\u0627 \u0634\u062f\u0647 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0633\u0639\u06cc \u06a9\u0646\u062f \u0645\u0634\u06a9\u0644\u0627\u062a \u063a\u06cc\u0631 \u0642\u0627\u0628\u0644 \u062d\u0644 \u0631\u0627 \u062d\u0644 \u06a9\u0646\u062f \u06cc\u0627 \u0628\u062f\u062a\u0631 \u0627\u0632 \u0622\u0646 \u0627\u06cc\u0646\u062a\u0631\u0646\u062a \u0631\u0627 \u0645\u0635\u0631\u0641 \u06a9\u0646\u062f.<\/em><\/p>\n
\ud83d\udd39 \u062f\u0631 Constrict-ion \ud83d\udc0d<\/strong><\/p>\n
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\u0648 \u0628\u0631\u0627\u06cc \u0647\u0645\u0647 \u0631\u0627\u0645\u200c\u06a9\u0646\u0646\u062f\u0647\u200c\u0647\u0627\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648 \u062c\u0627\u062f\u0648\u06af\u0631\u0627\u0646 \u062f\u0627\u062f\u0647\u060c \u0645\u0648\u0627\u0631\u062f \u0628\u06cc\u0634\u062a\u0631\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f!  \ud83c\udf1f \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0647\u0627\u06cc \u062c\u0630\u0627\u0628 \u0648 \u0645\u0627\u062c\u0631\u0627\u062c\u0648\u06cc\u06cc \u062f\u0631 \u062f\u0646\u06cc\u0627\u06cc \u0639\u0631\u0641\u0627\u0646\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u060c \u0628\u0647 PAIton \u0648 Crossovers \u0628\u0631\u0648\u06cc\u062f.  \u0628\u0647 \u0642\u0644\u0645\u0631\u0648\u0647\u0627\u06cc \u0645\u062d\u0631\u0645\u0627\u0646\u0647 \u06a9\u062f\u060c \u062c\u0627\u06cc\u06cc \u06a9\u0647 \u062c\u0627\u062f\u0648 \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f\u060c \u0644\u063a\u0632\u0634 \u06a9\u0646\u06cc\u062f!  \ud83c\udfa9\ud83d\udc0d\ud83d\udcbb<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"
\u062e\u0648\u0634 \u0622\u0645\u062f\u06cc\u062f\u060c \u0631\u0627\u0645 \u06a9\u0646\u0646\u062f\u06af\u0627\u0646 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648 \u062f\u0627\u0648\u06cc\u0646\u0686\u06cc\u0633 \u0628\u0627\u06cc\u062a\u06cc! \ud83c\udfa8 \u0627\u0645\u0631\u0648\u0632\u060c \u0645\u0627 \u062f\u0631 \u062d\u0627\u0644 \u063a\u0648\u0627\u0635\u06cc \u062f\u0631 \u062f\u0646\u06cc\u0627\u06cc \u0645\u0628\u0647\u0645 \u0648 \u0645\u0631\u0645\u0648\u0632 \u0647\u0648\u0634 \u0645\u0627\u0634\u06cc\u0646\u06cc \u0647\u0633\u062a\u06cc\u0645. \u0622\u06cc\u0627 \u0645\u0627 \u06cc\u06a9 \u0645\u0627\u0631 \u0645\u062a\u0641\u06a9\u0631 \u0627\u06cc\u062c\u0627\u062f \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f\u061f \ud83d\udc0d \u06cc\u0627 \u06cc\u06a9 \u062f\u0627\u0646\u0634\u0645\u0646\u062f \u0645\u0635\u0646\u0648\u0639\u06cc \u06a9\u0647 \u0627\u0632 \u0647\u0645\u0647 \u0645\u0627 \u067e\u06cc\u0634\u06cc \u0645\u06cc \u06af\u06cc\u0631\u062f\u061f \u0641\u0642\u0637 \u0632\u0645\u0627\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u06af\u0648\u06cc\u062f! \ud83d\udd39 \u0647\u0648\u0634 \u0645\u0627\u0634\u06cc\u0646\u06cc: \u0622\u06cc\u0627 \u0627\u06cc\u0646 \u06cc\u06a9 \u062c\u0627\u062f\u0648\u06cc \u0645\u0627\u0631 …<\/p>\n","protected":false},"author":2,"featured_media":26186,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[339],"tags":[],"class_list":["post-26185","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dev"],"_links":{"self":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/26185","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/comments?post=26185"}],"version-history":[{"count":0,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/26185\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media\/26186"}],"wp:attachment":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media?parent=26185"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/categories?post=26185"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/tags?post=26185"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}