* Cantinho Satkeys

Refresh History
  • JPratas: dgtgtr Pessoal  49E09B4F k7y8j0
    06 de Novembro de 2024, 17:19
  • FELISCUNHA: Votos de um santo domingo para todo o auditório  4tj97u<z
    03 de Novembro de 2024, 10:49
  • j.s.: bom fim de semana  43e5r6 49E09B4F
    02 de Novembro de 2024, 08:37
  • j.s.: ghyt74 a todos  4tj97u<z
    02 de Novembro de 2024, 08:36
  • FELISCUNHA: ghyt74   49E09B4F  e bom feriado   4tj97u<z
    01 de Novembro de 2024, 10:39
  • JPratas: try65hytr Pessoal  h7ft6l k7y8j0
    01 de Novembro de 2024, 03:51
  • j.s.: try65hytr a todos  4tj97u<z
    30 de Outubro de 2024, 21:00
  • JPratas: dgtgtr Pessoal  4tj97u<z k7y8j0
    28 de Outubro de 2024, 17:35
  • FELISCUNHA: Votos de um santo domingo para todo o auditório  k8h9m
    27 de Outubro de 2024, 11:21
  • j.s.: bom fim de semana   49E09B4F 49E09B4F
    26 de Outubro de 2024, 17:06
  • j.s.: dgtgtr a todos  4tj97u<z
    26 de Outubro de 2024, 17:06
  • FELISCUNHA: ghyt74   49E09B4F  e bom fim de semana
    26 de Outubro de 2024, 11:49
  • JPratas: try65hytr Pessoal  101yd91 k7y8j0
    25 de Outubro de 2024, 03:53
  • JPratas: dgtgtr A Todos  4tj97u<z 2dgh8i k7y8j0
    23 de Outubro de 2024, 16:31
  • FELISCUNHA: ghyt74  pessoal   49E09B4F
    23 de Outubro de 2024, 10:59
  • j.s.: dgtgtr a todos  4tj97u<z
    22 de Outubro de 2024, 18:16
  • j.s.: dgtgtr a todos  4tj97u<z
    20 de Outubro de 2024, 15:04
  • FELISCUNHA: Votos de um santo domingo para todo o auditório  101041
    20 de Outubro de 2024, 11:37
  • axlpoa: hi
    19 de Outubro de 2024, 22:24
  • FELISCUNHA: ghyt74   49E09B4F  e bom fim de semana  4tj97u<z
    19 de Outubro de 2024, 11:31

Autor Tópico: Working with Multidimensional Data Using NumPy  (Lida 43 vezes)

0 Membros e 1 Visitante estão a ver este tópico.

Online mitsumi

  • Moderador Global
  • ***
  • Mensagens: 115915
  • Karma: +0/-0
Working with Multidimensional Data Using NumPy
« em: 27 de Dezembro de 2022, 12:08 »


Janani Ravi | Duration: 1:43 h | Video: H264 1280x720 | Audio: AAC 44,1 kHz 2ch | 207 MB | Language: English

As working with huge numeric datasets becomes the norm, using the right tools and libraries to work with the data becomes very important. NumPy allows data analysts and data scientists to work with multi-dimensional data to solve these problems.
As machine learning and deep learning techniques become popular, getting the dataset into the right numeric form and engineering the right features to feed into ML models becomes critical.
In this course, Working with Multidimensional Data Using NumPy, you'll learn the simple and intuitive functions and classes that NumPy offers to work with data of high dimensionality.
First, you will get familiar with basic operations to explore multi-dimensional data, such as creating, printing, and performing basic mathematical operations with arrays. You'll study indexing and slicing of array data and iterating over lists and see how images are basically 3D arrays and how they can be manipulated with NumPy.
Next, you will move on to complex indexing functions. NumPy arrays can be indexed with conditional functions as well as arrays of indices. You'll then see how broadcasting rules work which allows NumPy to perform operations on arrays with different shapes as well as, study array operations such as np.argmax() which are very common when working with ML problems.
Finally, you'll study how NumPy integrates with other libraries in the PyData stack. You will also cover specific implementations with SciPy and with Pandas.
At the end of this course, you will be comfortable using the array manipulation techniques that NumPy has to offer to get your data in the right form for extracting insights.


Download link

rapidgator.net:
Citar
https://rapidgator.net/file/c655cde88f2d7a5947877048a6a73a7f/armxf.Working.with.Multidimensional.Data.Using.NumPy.rar.html

uploadgig.com:
Citar
https://uploadgig.com/file/download/9b5234b17b1C5426/armxf.Working.with.Multidimensional.Data.Using.NumPy.rar

nitroflare.com:
Citar
https://nitroflare.com/view/F5BF73654BC07FE/armxf.Working.with.Multidimensional.Data.Using.NumPy.rar