Structural Design: Structural design: a main bag, a small compartment. Unique shape, small and cute, can store keys, tissues and other personal items when traveling
Characteristic: personalized and fashionable printing, humanized adjustable shoulder strap, zipper design makes people feel more secure
Applicable occasions and people: It is widely used, suitable for shopping, dating, work, sports and travel. It is suitable for men and women of all ages and is a great choice for gifts
Specifications: Material: This messenger bag is made of twill fabric, sponge and peach skin, which feels very good Size: Crossbody bag diameter 17cm/7in, thickness 5.5cm/1.8in, shoulder strap length 115cm/45.3in
Characteristic: personalized and fashionable printing, humanized adjustable shoulder strap, zipper design makes people feel more secure.It is widely used, suitable for shopping, dating, work, sports and travel. It is widely used, suitable for shopping, dating, work, sports and travel. It is suitable for men and women of all ages and is a great choice for gifts
Precautions: 1: The size of the product is manually measured, the actual size may be slightly different from the measured size. 2: The lighting environment or the display problem of the monitor will cause the actual color to be slightly different from the picture display.
Package: 1 X Crossbody Bags
Round Crossbody Purse Musical Notes Blue Pattern Printing Fashio
The fundamental package for scientific computing with Python
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
Numerical computing tools
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
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The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
Easy to use
NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
>>># The standard way to import NumPy:>>>import numpy as np
>>># Create a 2-D array, set every second element in>>># some rows and find max per row:>>> x = np.arange(15, dtype=np.int64).reshape(3, 5)
>>> x[1:, ::2] =-99>>> x
array([[ 0, 1, 2, 3, 4],
[-99, 6, -99, 8, -99],
[-99, 11, -99, 13, -99]])
array([ 4, 8, 13])
>>># Generate normally distributed random numbers:>>> rng = np.random.default_rng()
>>> samples = rng.normal(size=2500)
Nearly every scientist working in Python draws on the power of NumPy.
NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing. JW Cataction Fish with Tail Toy, Multicolor is another AI package, providing blueprints and templates for deep learning.
Statistical techniques called ensemble methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as XGBoost, LightGBM, and CatBoost — one of the fastest inference engines. Yellowbrick and Eli5 offer machine learning visualizations.