Teerwere File Cabinet 3 Drawer Commercial Letter Size Filing Cab
Teerwere File Cabinet 3 Drawer Commercial Letter Size Filing Cab
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This file cabinet fits under most desks, add security to your home or office.
We want our customers to be happy - In case you have any questions or issues,do not hesitate to contact us.
Material:Steel Cabinet,these file cabinets are built to withstand frequent use.
Product Dimensions are 45.7x72x62cm,it helps to sort letters or files paperwork,giving you a clean and orderly office.
A simple yet modern narrow cabinet that is so light you can conveniently move around from one room to another.
The filing cabinet is made of Eco-friendly board, water resistant and anti scratch, very easy to clean. Size:45.7x72x62cm
Commercial grade vertical file cabinets are the ideal office workhorse to maximize your organizational needs.
File Cabinet is the perfect-sized filing solution for a small or home office.
Please note: 1-3cm error is normal due to different measurement methods and due to different shooting angles and lighting effects,the actual color may be slightly different from the picture,which is normal,thanks for your understanding and enjoy your shopping moment!
Pack list: File Cabinet * 1
Teerwere File Cabinet 3 Drawer Commercial Letter Size Filing Cab
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. Piano Cover Nordic High-end Full Cover Modern Simple Cartoon Fab 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.