Scratching Data Science in Weekend
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Scratching Data Science in Weekend

About the Bundle

This bundle is design to invest your weekend in studying data science.

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  • Categories

    • Artificial Intelligence
    • Data Science

About the Books

Scratching Linear Algebra in Weekend

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  • PDF

Important Note: Please if you want to buy it, go to the bundle with the same price, and get the whole series.

In this part, you will learn the pre-requisites linear algebra that needed for both of machine learning and linear algebra, you will study both of vectors and matrices and the operations that can be use in both of vectors and matrices. For example, addition, summation, subtraction and multiplication, also we will study advanced operations. With the theories we will give you the implementation of all these operations from scratch, to enhance your skills in both of linear algebra and coding too.

You can check the first part A Refresher Guide to Convolution Neural Networks here

A Refresher Guide to Convolution Neural Networks

A Part of Weekend Series
  • 126


  • 100%


  • PDF

  • EPUB

The goal of any Convolution Neural Network is to learn higher-order features from data representation, achieving that via convolutions. This type of Neural Nets is very good in dealing with tensor data such as images and is well suited to object recognition with consistently top image classification competitions. In this part, I will try to teach you the convolution neural network on weekend.

An online and free version of this part can be found here.

You can check the Scratching Linear Algebra in Weekend here.

Also, do not forget to check the following weekends:

Python Basics

Python OOP


Pandas I.



Introduction to Data

Data Visualization and Understanding

Data Cleaning and Preprocessing Techniques.

Data Exploration and Analysis.

Data Exploration Part I.

Data Exploration Part II.

Data Exploration, It's all about Data Wrangling.

The Art of Data Visualization.

Rules of data visualization.

Linear Algebra.

Math for Machine Learning.

Gradient Descent.

Linear Regression.

Logistic Regression.

Support Vector Machines.

Tree Methods.

Ensemble Methods.

Introduction to Deep Learning.

Vanilla Neural Network, A refresher guide.

Convolution Neural Network, A refresher guide.

Recurrent Neural Network, A refresher guide.

Attention Mechanism, A refresher guide.

Auto Encoders, A refresher guide.

Generative Adversarial Networks, A refresher guide.

Shehzada (Shaz-z)

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