Process of compiling C++ code (i)Preprocessing : The preprocessor analyzes the source code in the first step of the compilation process and performs tasks such as including header files, expanding macros, and handling conditional compilation statements. (ii)Parsing : The next step is parsing, where the compiler checks the syntax of the source code and creates an abstract syntax tree (AST) to represent the code. Semantic analysis : The compiler analyzes the AST in this step and performs tasks such as type checking, name resolution, and error checking to ensure the code is valid and executable. (iii)Optimization : After analyzing and validating the code, the compiler may perform various optimizations to enhance the performance of the resulting code. These optimizations include removing dead code, inlining functions, and reordering code to improve cache performance. Code generation: Finally, the compiler generates object code that the computer can execute. This object code is ...
ARRAYS An array in C/C++ or be it in any programming language is a collection of similar data items stored at contiguous memory locations and elements can be accessed randomly using indices of an array. There are two types of array in C++, which are : Single-dimensional array : It is a collection of elements of the same data type that are stored in a contiguous block of memory. Multi-dimensional array : It is an array that contains one or more arrays as its elements. We will see this in the next section multidimensional arrays in C++ .
Very Useful Information In the dynamic landscape of data science, Python remains the go-to programming language for its versatility and extensive ecosystem of libraries. As we step into 2024, the Python data science toolkit continues to evolve, with new libraries and updates enhancing the capabilities of professionals in the field. 1. TensorFlow 2.x 2. PyTorch 3. Pandas 4. Scikit - Learn 5. Dask 6. Statsmodels 7. Matplotlib and Seaborn 8. XGBoost 9. NLTK (Natural Language Toolkit) 10. Plotly
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