Financial Modeling & Analysis

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

Deep Learning is an area of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with many layers of non-linear nodes. In the field of Machine Learning, Deep Networks have found many applications and have been able to tackle problems which were previously believed to require human level intelligence for solution – such as object recognition – convincingly. In this post, I will guide you through the prerequisites needed to start building your own Deep Networks from scratch in Python…

## What you’ll learn in Deep Learning Requirementsites: The Numpy Stack in Python (V2+)

• Using Scikit-Learn and real-world examples, learn about supervised machine learning (classification and regression).
• Learn how to use Numpy and write code with it.
• Numerical algorithms can be implemented with Numpy, Scipy, Matplotlib, and Pandas.
• Learn about the advantages and disadvantages of different machine learning models, such as Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and more!

## Requirements

• Recognize the Gaussian distribution and linear algebra.
• Python coding experience is required.
• You should already be aware of the “why” and “what” of concepts such as the dot product, matrix inversion, and Gaussian probability distributions.

## Description

This is the Numpy Stack in Python, which is required for Deep Learning, Machine Learning, and Data Science.
People want to learn deep learning and data science, so they enroll in these courses, but they fall behind because they don’t understand the Numpy stack well enough to translate those concepts into code.

This course will show you how to do things in the Numpy stack that are frequently needed in deep learning and data science, in order to remove that barrier.
So, what exactly are they?
The Numpy array, on which you can perform various operations, is the central object in Numpy.

## 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
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