15"X15" Square Seat Cushion Patio Outdoor Indoor Chair Pads with
15"X15" Square Seat Cushion Patio Outdoor Indoor Chair Pads with
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Size:15 x 15 x 1.6inches(38 x 38 x 4cm)
Extra Soft: Microfiber polyester cover, soft and durable, not easy to fade; With a 100% natural pearl cotton filling, fluffy and breathable. Give you a super comfortable experience. That is different from regular seat chair pads which filled with synthetic fibers may cause you to feel unwell.
Widely Use: Decorate your house that makes it looks so colorful and gorgeous. It can be used for dining chairs, office chairs, sofas, tatami, benches, wicker chair, bay window, rocking chairs, patio chairs, and car seats. Relieving the discomfort caused by sitting long.
Antiskid Design: There are two straps behind the cushion, you can conveniently secure the ergonomic chair pad to your seat and never have to deal with any slipping or sliding.
Easy to Clean: Just hand wash with mild detergent and air dry. Warm and Sweet reminders---Do not bleach, do not iron, do not dry clean.
EXTRA-COMFY AND SOFT
Microfiber polyester cover and pearl cotton filling make the chair pillows extra comfortable, extra soft, and impressively supportive.
Precise sewing and cutting, fashionable and exquisite; environmentally friendly printing and dyeing process, full color, not easy to fade.
PERFECT FIT FOR ANY CHAIR
These cushions are square shape, and measure 15" x 15". These dimensions are a perfect fit for almost all dining room, living room, playroom, bedroom, and even outdoor chairs
Just wash with mild detergent and air dry.
Do not bleach, do not iron, do not dry clean just tumble dry with no heat and your chair pads will look as good as new.
Material: Microfiber Polyester
Size: 15 x 15 x 1.6inches(38 x 38 x 4cm)
15"X15" Square Seat Cushion Patio Outdoor Indoor Chair Pads with
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. LPVLUX Halloween Poster Mummy Forest Shower Curtain, Shower Curt 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.