Udemy - Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.7 GB
  • Uploaded Bytutsnode
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  • Last checkedDec. 17th '20
  • Date uploadedDec. 14th '20
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Infohash : A3C1BC603EA23395D4A2C73E6F5E531D99DCA02F


Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.

One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.

Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.

This course is designed to remove that obstacle – to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.

So what are those things?

Numpy. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations.

The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.

That means you can do vector and matrix operations like addition, subtraction, and multiplication.

The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.

Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

Pandas. Pandas is great because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually.

Pandas makes working with datasets a lot like R, if you’re familiar with R.

The central object in R and Pandas is the DataFrame.

We’ll look at how much easier it is to load a dataset using Pandas vs. trying to do it manually.

Then we’ll look at some dataframe operations, like filtering by column, filtering by row, the apply function, and joins, which look a lot like SQL joins.

So if you have an SQL background and you like working with tables then Pandas will be a great next thing to learn about.

Since Pandas teaches us how to load data, the next step will be looking at the data. For that we will use Matplotlib.

In this section we’ll go over some common plots, namely the line chart, scatter plot, and histogram.

We’ll also look at how to show images using Matplotlib.

99% of the time, you’ll be using some form of the above plots.

Scipy.

I like to think of Scipy as an addon library to Numpy.

Whereas Numpy provides basic building blocks, like vectors, matrices, and operations on them, Scipy uses those general building blocks to do specific things.

For example, Scipy can do many common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

It has signal processing tools so it can do things like convolution and the Fourier transform.

In sum:

If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

Suggested Prerequisites:

matrix arithmetic
probability
Python coding: if/else, loops, lists, dicts, sets
you should already know “why” things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses)

Who this course is for:

Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code

Requirements

Understand linear algebra and the Gaussian distribution
Be comfortable with coding in Python
You should already know “why” things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

Last Updated 11/2020

Files:

Deep Learning Prerequisites The Numpy Stack in Python (V2+) [TutsNode.com] - Deep Learning Prerequisites 08 Setting Up Your Environment (FAQ by Student Request)
  • 044 Windows-Focused Environment Setup 2018.mp4 (186.2 MB)
  • 044 Windows-Focused Environment Setup 2018.en.srt (20.9 KB)
  • 045 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.en.srt (15.1 KB)
  • 045 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (43.9 MB)
01 Welcome and Logistics
  • 001 Introduction and Outline.en.srt (12.2 KB)
  • 001 Introduction and Outline.mp4 (60.5 MB)
  • 002 Extra Resources.en.srt (5.0 KB)
  • 002 Extra Resources.mp4 (25.3 MB)
02 Numpy (New)
  • 003 Numpy Section Introduction.en.srt (8.0 KB)
  • 003 Numpy Section Introduction.mp4 (24.1 MB)
  • 004 Arrays vs Lists.en.srt (15.1 KB)
  • 004 Arrays vs Lists.mp4 (50.8 MB)
  • 005 Dot Product.en.srt (7.4 KB)
  • 005 Dot Product.mp4 (26.3 MB)
  • 006 Speed Test.en.srt (3.9 KB)
  • 006 Speed Test.mp4 (14.7 MB)
  • 007 Matrices.en.srt (17.0 KB)
  • 007 Matrices.mp4 (57.2 MB)
  • 008 Solving Linear Systems.en.srt (4.3 KB)
  • 008 Solving Linear Systems.mp4 (14.4 MB)
  • 009 Generating Data.en.srt (18.0 KB)
  • 009 Generating Data.mp4 (63.2 MB)
  • 010 Numpy Exercise.en.srt (1.4 KB)
  • 010 Numpy Exercise.mp4 (4.7 MB)
  • 011 Where to Learn More Numpy.en.srt (10.5 KB)
  • 011 Where to Learn More Numpy.mp4 (38.9 MB)
  • 012 Suggestion Box.en.srt (4.9 KB)
  • 012 Suggestion Box.mp4 (16.1 MB)
03 Matplotlib (New)
  • 013 Matplotlib Section Introduction.en.srt (3.8 KB)
  • 013 Matplotlib Section Introduction.mp4 (14.7 MB)
  • 014 Line Chart.en.srt (3.9 KB)
  • 014 Line Chart.mp4 (16.2 MB)
  • 015 Scatterplot.en.srt (5.0 KB)
  • 015 Scatterplot.mp4 (17.9 MB)
  • 016 Histogram.en.srt (2.4 KB)
  • 016 Histogram.mp4 (10.3 MB)
  • 017 Plotting Images.en.srt (8.6 KB)
  • 017 Plotting Images.mp4 (38.6 MB)
  • 018 Matplotlib Exercise.en.srt (2.3 KB)
  • 018 Matplotlib Exercise.mp4 (12.4 MB)
  • 019 Where to Learn More Matplotlib.en.srt (18.1 KB)
  • 019 Where to Learn More Matplotlib.mp4 (77.1 MB)
04 Pandas (New)
  • 020 Pandas Section Introduction.en.srt (1.7 KB)
  • 020 Pandas Section Introduction.mp4 (5.9 MB)
  • 021 Loading in Data.en.srt (3.9 KB)
  • 021 Loading in Data.mp4 (23.6 MB)
  • 022 Selecting Rows and Columns.en.srt (10.9 KB)
  • 022 Selecting Rows and Columns.mp4 (49.1 MB)
  • 023 The apply() Function.en.srt (2.6 KB)
  • 023 The apply() Function.mp4 (10.4 MB)
  • 024 Plotting with Pandas.en.srt (2.7 KB)
  • 024 Plotting with Pandas.mp4 (13.4 MB)
  • 025 Pandas Exercise.en.srt (3.0 KB)
  • 025 Pandas Exercise.mp4 (13.5 MB)
  • 026 Where to Learn More Pandas.en.srt (6.2 KB)
  • 026 Where to Learn More Pandas.mp4 (25.6 MB)
05 Scipy (New)
  • 027 Scipy Section Introduction.en.srt (1.8 KB)
  • 027 Scipy Section Introduction.mp4 (6.3 MB)
  • 028 PDF and CDF.en.srt (3.1 KB)
  • 028 PDF and CDF.mp4 (14.5 MB)
  • 029 Convolution.en.srt (4.7 KB)
  • 029 Convolution.mp4 (21.5 MB)
  • 030 Scipy Exercise.en.srt (1.4 KB)
  • 030 Scipy Exercise.mp4 (6.4 MB)
  • 031 Where to Learn More Scipy.en.srt (10.6 KB)
  • 031 Where to Learn More Scipy.mp4 (38.9 MB)
06 Bonus Exercises
  • 032 More Exercises.en.srt (10.9 KB)
  • 032 More Exercises.mp4 (16.7 MB)
07 Machine Learning Basics
  • 033 Machine Learning_ Section Introduction.en.srt (11.6 KB)
  • 033 Machine Learning_ Section Introduction.mp4 (38.9 MB)
  • 034 What is Classification_.en.srt (17.3 KB)
  • 034 What is Classification_.mp4 (62.2 MB)
  • 035 Classification in Code.en.srt (17.2 KB)
  • 035 Classification in Code.mp4 (125.4 MB)
  • 036 What is Regression_.en.srt (17.4 KB)
  • 036 What is Regression_.mp4 (43.2 MB)
  • 037 Regression in Code.en.srt (10.3 KB)
  • 037 Regression in Code.mp4 (61.9 MB)
  • 038 What is a Feature Vector.en.srt (9.4 KB)
  • 038 What is a Feature Vector.mp4 (32.8 MB)
  • 039 Machine Learning is Nothing but Geometry.en.srt (6.3 KB)
  • 039 Machine Learning is Nothing but Geometry.mp4 (19.2 MB)
  • 040 All Data is the Same.en.srt (7.5 KB)
  • 040 All Data is the Same.mp4 (21.3 MB)
  • 041 Comparing Different Machine Learning Models.en.srt (13.6 KB)
  • 041 Comparing Different Machine Learning Models.mp4 (45.5 MB)
  • 042 Machine Learning and Deep Learning_ Future Topics.en.srt (8.2 KB)
  • 042 Machine Learning and Deep Learning_ Future Topics.mp4 (36.7 MB)
  • 043 Machine Learning Section Summary.en.srt (8.3 KB)
  • 043 Machine Learning Section Summary.mp4 (21.4 MB)
09 Extra Help With Python Coding for Beginners (FAQ by Student Request)
  • 046 Python 2 vs Python 3.en.srt (6.3 KB)
  • 046 Python 2 vs Python 3.mp4 (19.1 MB)
  • 047 Proof that using Jupyter Notebook is the same as not using it.en.srt (14.7 KB)
  • 047 Proof that using Jupyter Notebook is the same as not using it.mp4 (78.3 MB)
10 Effective Learning Strategies for Machine Learning (FAQ by Student Request)
  • 048 Machine Learning and AI Prerequisite Roadmap (pt 1).en.srt (16.6 KB)
  • 048 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (29.3 MB)
  • 049 Machine Learning and AI Prerequisite Roadmap (pt 2).en.srt (23.9 KB)
  • 049 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (37.6 MB)
11 Appendix _ FAQ Finale
  • 050 BONUS_ Where to get Udemy coupons and FREE deep learning material.en.srt (8.2 KB)
  • 050 BONUS_ Where to get Udemy coupons and FREE deep learning material.mp4 (37.9 MB)

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