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Deep Learning Prerequisites [What Else Apart from Programming & Statistics?]

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29th Jul, 2020
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Deep Learning Prerequisites [What Else Apart from Programming & Statistics?]

As deep learning is among the most advanced concepts in the tech sector, it has plenty of prerequisites. In this article, we’ll be discussing the various subjects you should be familiar with before you begin studying deep learning. Some of them are branches of mathematics while some others are separate disciplines. Let’s start:

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Deep Learning Prerequisites

1. Programming

Programming is the fundamental requirement of deep learning. You can’t perform deep learning without using a programming language. Deep learning professionals use Python or R as their programming language because of their functionalities and effectiveness. Before you study the various concepts of deep learning, you’ll have to study programming and get familiar with one of these two prominent languages. 

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Both of these languages are entirely different in terms of their applications as well. Python is a versatile language that finds applications in data science, ML, as well as app development. On the other hand, R is a more focused language and finds uses in data science and AI correctly. A general understanding of how these programming languages work and how to use them is a must to become deep learning professional. 

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2. Statistics

Statistics refer to the study of using data and its visualization. It helps in gaining information from the raw data you have. Statistics is a crucial part of data science (which we’ve discussed later) and its relevant disciplines. As a deep learning professional, you’d have to gain insights from data for which you’ll need to use statistics. 

In statistics, you plot charts, create graphs, and understand relations between different data points. It also helps you gain insights from samples of data and classifying the available information in different segments according to your requirements. 

3. Calculus

Calculus forms the basis for many machine learning algorithms. So, you’ll have to study calculus too as a part of deep learning prerequisites. In deep learning, you’ll be building models according to the features present in your data. Calculus will help you in using those features and making the model accordingly. 

Having a basic understanding of calculus, integration, and other topics can help you in becoming a better ML expert. However, as a deep learning professional, you’ll mainly need to study the basic principles of calculus and not its advanced concepts. 

4. Linear Algebra

Probably one of the most important deep learning prerequisites is linear algebra. Linear algebra deals with matrices, vectors, and linear equations. It focuses on the representation of linear equations in vector spaces. The linear algebra will help you in building models of various sorts (classification, regression, etc.), and it is another building block for numerous concepts of deep learning. 

5. Probability

Probability is a branch of mathematics that focuses on describing how likely an event can occur or how possible it is valid through numbers. The probability of any event ranges from 0 to 1, where 0 indicates impossibility, and 1 represents absolute certainty. 

In ML and deep learning, you have to build models for predictive analysis. You have to train them to predict specific outcomes. That’s why probability is an essential subject to study for a deep learning student.

Check out: Deep Learning Project Ideas for Beginners

6. Data Science

Data science is the field of analyzing and using data to gain valuable insights. As a deep learning professional, you must be familiar with various concepts of data science as you’d have to build models that handle data. Knowing deep learning will help you in using data to get the desired results, but before using deep learning, you’ll have to learn about data science. 

The two most programming languages necessary for data science are Python and R. Although data science is a vast subject and covers many topics along with deep learning, you must know its basics first. Data science helps companies in making predictions about customer behavior, sales, and market trends. This is just one example of how vital data science is, so you must be familiar with it to move onto deep learning. 

7. Work on Projects

While learning these subjects will help you in building a strong foundation, you will also have to work on deep learning projects to make sure you understand everything correctly. Projects will help you in applying what you’ve learned and identified your weak areas. Deep learning finds applications in multiple areas so you can easily find a project of your interest. 

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The Best Way to Study Deep Learning

The topics we discussed here are just the basics, and deep learning has many concepts you must learn. Many students feel overwhelmed because of this and wonder, “How do I study all of this?” The best way to do that is through a deep learning course. Courses have detailed syllabuses and enable you to learn directly from the experts and deep learning professionals. For example, in our deep learning course, you get to study all of these prerequisites along with some additional topics to make you a full-fledged professional such as Neural Networks, Clustering algorithms, regression, etc. 

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Also Read: Deep Learning Salary in India

Final Thoughts

We hope you found this article helpful. If you have any questions regarding this topic or the subjects we’ve shared here, feel free to let us know. We’d love to hear your thoughts.

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What is deep learning?

Deep learning is a machine learning technique used to obtain a more accurate predictive model of your data, which can then be used to predict with higher accuracy how users will behave. It works by building a hierarchical model called a deep neural network. It consists of multiple processing layers, each layer consists of multiple neurons that interact with each other. It is used in a broad range of applications, including computer vision (self-driving cars), speech recognition (virtual assistants), and in recommendation systems.

2What are the prerequisites to learn deep learning?

First, you need to have knowledge of how machine learning works. The second requirement is to have a basic understanding of computer programming. You don't need to be an expert in programming (there are already many languages specialized in machine learning), but you will need to know the basics of how a computer works and how it uses data to make decisions. We also recommend you to learn some basic math. Even if you don't plan on pursuing a career in mathematics, knowledge of some basic math will be very useful. Since machine learning is based on statistics and probability, learning some statistics and probability will help you understand machine learning better.

3What is the future of deep learning?

Deep learning is used across industries ranging from medical to e-commerce. In the medical industry, deep learning is used to identify cancerous growths in MRIs, for example. In e-commerce, deep learning is used to determine which advertisements and products to display to customers. The two major challenges facing deep learning technology today are transparency and bias. Transparency is the ability for a human to understand the reasoning behind a machine-made decision. Bias is when a machine is consistently favoring a certain outcome. Because of these challenges, the future of deep learning technology is uncertain.

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