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#INSTALL IPYTHON 6.2.1 CODE#
Code cellsĬan contain code which can be executed with a simple click (or Notebooks consists of code cells and markdown cells. Through an interactive web-based environment jupyter notebook. In recent years, it has become increasingly popular to use python The code is in the code cell, output of it is The same program as above running in a Jupyter notebook (through Virtual environments that make this process simpler, but unfortunatelyĪll too often it happens that the different versions still manage to There are tools, such as anaconda environments or python Old versions of libraries as some of the code only works with older Has to keep different versions of python installed in order to support This may easily lead to installation hell, where the user If you haven’t beenĬontinuously updating your code for last few years, expect to run intoįinally, due to the rapid development, python itself is also rapidlyĬhanging. Unfortunate result of breaking the old code.
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For example, tensorflow is developing rapidly with an Problems is related to rapid development of certain popularįrameworks. But it is also a source of frequent confusion. Sometimes very handy, in particular when handling large data Modify data “in place”, i.e. without doing a copy in memory. Power-user tools that are designed for power-users, not for beginners.įor instance, many python libraries have options to The other source of the complexity are the Libraries per se, but to the fact the data-processing concepts are Part of the problem is not related to python or the Python has a rich infrastructure of libraries, including data Of the other rigor of those language, so it is a much better choice for Unlike C++ or java (and like R), python is weakly typed and skips much Includes a number of power-user tools, such as passing by reference, thatĪllows it to handle data more efficiently than more functional It is in many ways similar to C++ and java, butĪvoids a number of complexities of those languages. Python is a general programming language which in recent years has gained a lot of 15.6.2 Confusion Matrix–Based Model Goodness Measures.15.6.1 Predicting with Logistic Regression.15.4.1 Numpy Arrays as Vectors and Matrices.14.2.1 Bag-of-words and Document-term-matrix.14 Natural Language Processing: Text As Data.13.2.3 Image processing with keras convolutional networks.13.2 Convolutional Neural Networks in Keras.10.1 How highly correlated features fail.10 Regularization and Feature Selection.9.3 Confusion Matrix–Based Model Goodness Measures.
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3.2.4 Selecting observations by position.3.1.4 Universal Functions (Vectorized Functions).3.1.2 Array: The Fundamental Data Structure in Numpy.2.3.4 Mathematical, logical and other operators.2.3.1 A few words about variable names and coding style.