DEEP LEARNING PREREQUISITES THE NUMPY STACK IN PYTHON

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DEEP LEARNING PREREQUISITES THE NUMPY STACK IN PYTHON

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep studying, machine studying, and synthetic intelligence

What you’ll study in DEEP LEARNING PREREQUISITES THE NUMPY STACK IN PYTHON

  • Perceive supervised machine studying (classification and regression) with real-world examples utilizing Scikit-Be taught
  • Perceive and code utilizing the Numpy stack
  • Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
  • Perceive the professionals and cons of varied machine studying fashions, together with Deep Studying, Choice Timber, Random Forest, Linear Regression, Boosting, and Extra!

Necessities

  • Perceive linear algebra and the Gaussian distribution
  • Be comfy with coding in Python
  • It is best to already know “why” issues like a dot product, matrix inversion, and Gaussian likelihood distributions are helpful and what they can be utilized for

Description

Welcome! That is Deep Studying, Machine Studying, and Information Science Conditions: The Numpy Stack in Python.

One query or concern I get so much is that individuals wish to study deep studying and information science, in order that they take these programs, however they get left behind as a result of they don’t know sufficient concerning the Numpy stack with the intention to flip these ideas into code.

Even when I write the code in full, should you don’t know Numpy, then it’s nonetheless very exhausting to learn.

This course is designed to take away that impedimentto indicate you how one can do issues within the Numpy stack which can be incessantly wanted in deep studying and information science.

So what are these issues?

Numpy. This varieties the premise for all the pieces else.  The central object in Numpy is the Numpy array, on which you are able to do numerous operations.

The secret’s {that a} Numpy array isn’t only a common array you’d see in a language like Java or C++, however as an alternative is sort of a mathematical object like a vector or a matrix.

Meaning you are able to do vector and matrix operations like addition, subtraction, and multiplication.

A very powerful facet of Numpy arrays is that they’re optimized for velocity. So we’re going to do a demo the place I show to you that utilizing a Numpy vectorized operation is quicker than utilizing a Python record.

Then we’ll take a look at some extra sophisticated matrix operations, like merchandise, inverses, determinants, and fixing linear methods.

Pandas. Pandas is nice as a result of it does loads of issues below the hood, which makes your life simpler since you then don’t must code these issues manually.

Pandas makes working with datasets so much like R, should you’re conversant in R.

The central object in R and Pandas is the DataFrame.

We’ll take a look at how a lot simpler it’s to load a dataset utilizing Pandas vs. making an attempt to do it manually.

Then we’ll take a look at some dataframe operations, like filtering by column, filtering by row, the apply operate, and joins, which look so much like SQL joins.

So if in case you have an SQL background and you want working with tables then Pandas will probably be an amazing subsequent factor to find out about.

Since Pandas teaches us how one can load information, the subsequent step will probably be trying on the information. For that we’ll use Matplotlib.

On this part we’ll go over some widespread plots, specifically the road chart, scatter plot, and histogram.

We’ll additionally take a look at how one can present pictures utilizing Matplotlib.

99% of the time, you’ll be utilizing some type of the above plots.

Scipy.

I like to consider Scipy as an addon library to Numpy.

Whereas Numpy gives fundamental constructing blocks, like vectors, matrices, and operations on them, Scipy makes use of these normal constructing blocks to do particular issues.

For instance, Scipy can do many widespread statistics calculations, together with getting the PDF worth, the CDF worth, sampling from a distribution, and statistical testing.

It has sign processing instruments so it may well do issues like convolution and the Fourier rework.

In sum:

When you’ve taken a deep studying or machine studying course, and also you perceive the speculation, and you may see the code, however you possibly can’t make the connection between how one can flip these algorithms into precise working code, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • linear algebra
  • likelihood
  • Python coding: if/else, loops, lists, dicts, units
  • it’s best to already know “why” issues like a dot product, matrix inversion, and Gaussian likelihood distributions are helpful and what they can be utilized for

TIPS (for getting via the course):

  • Watch it at 2x.
  • Take handwritten notes. This may drastically enhance your means to retain the data. This has been confirmed by analysis!
  • Ask a number of questions on the dialogue board. The extra the higher!
  • Notice that the majority workout routines will take you days or perhaps weeks to finish.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Take a look at the lecture “What order ought to I take your programs in?” (accessible within the Appendix of any of my programs)

Who this course is for:

  • College students and professionals with little Numpy expertise who plan to study deep studying and machine studying later
  • College students and professionals who’ve tried machine studying and information science however are having hassle placing the concepts down in code

Created by Lazy Programmer Inc.
Final up to date 3/2019
English
English [Auto-generated]

Measurement: 852.70 MB

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Specification: DEEP LEARNING PREREQUISITES THE NUMPY STACK IN PYTHON

license

General Public License (GPL)

high-resolution

Yes

gutenberg-optimized

Yes

documentation

Well Documented

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Responsive

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DEEP LEARNING PREREQUISITES THE NUMPY STACK IN PYTHON
DEEP LEARNING PREREQUISITES THE NUMPY STACK IN PYTHON

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