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

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

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence

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What you'll learn

  • Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
  • Understand and code using the Numpy stack
  • Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
  • Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!

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

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.

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