Introduction to Neural Networks

What is a neural network? How does it work? What does a neural network do? Learn neural networks for free in this course and get your neural network questions answered, including applications of neural networks in deep learning.


Learn how neural networks work in deep learning

Do you want to acquire a super power? How about learning neural networks? Neural networks are at the heart of the deep learning revolution that’s happening around us right now.

Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.

But as a beginner in this field, you’ll have a ton of questions:

  • What is a neural network?
  • Why do we need to learn neural networks?
  • How popular are neural networks?
  • What are the advantages of neural networks?
  • What kind of challenges you could face when applying neural networks?
  • What exactly should you learn about neural networks?
  • What are the core concepts that make up neural networks?
  • What are the different types of neural networks in deep learning?
  • Do you need to know programming to build a neural network?
  • Which programming language is best for building neural networks? Python or R?
  • What are the different applications of neural networks?
  • What kind of problems or projects can you solve using neural networks?


From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us. Thanks to the idea of neural networks like CNN and RNN, deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis!

This free course by Analytics Vidhya will give you a taste of what a neural network is, how it works, what are the building blocks of a neural network, and where you can use neural networks. The perfect course for a beginner in deep learning!

Enroll for free now

So how can you get started with Neural networks?

Where should you begin learning? Neural networks can appear to be complex to master. In a lot of ways, they are. We have seen quite a number of aspiring data scientists and deep learning enthusiasts give up before they even touched a neural network!

But they’ve gone about it the wrong way. There are a lot of myths about neural networks that force people to quit:

  • You need a strong background in statistics, machine learning, linear algebra, and calculus to learn neural networks
  • You need to have a Ph.D. in order to understand neural networks
  • You cannot build a neural network without advanced mathematics knowledge

Mastering all of these would take you years!

So, how else can you learn neural networks?

That is EXACTLY what this free course by Analytics Vidhya will teach you.

Common Questions Deep Learning Beginners ask about Neural Networks

  • Why do we need to learn neural networks?

    Neural networks are at the core of the majority of deep learning applications. From computer vision use cases like facial recognition and object detection, to Natural Language Processing (NLP) tasks like writing essays and building human-like chatbots, neural networks are ubiquitous.

    The different offshoots of neural networks, such as convolutional neural networks (CNN), recurrent neural networks (RNNs), and Long Short Term Memory (LSTM), are a must-know.

    Neural networks are the present and the future - you should start learning right now!

  • Why are neural networks so popular?

    The above deep learning applications must have given you a fairly good idea of how useful neural networks are. They are incredibly powerful and organizations around the world are tapping into neural networks like CNN and RNN to help them find solutions to pressing problems.

    Here are a few key reasons why neural networks and deep learning have become mainstream:
    ● Cheaper computation cost in recent years means neural networks are accessible to a wider deep learning community
    ● Availability of larger datasets means we can truly harness the power of neural networks
    ● Neural networks have consistently shown better performance on Computer Vision, Natural Language Processing (NLP), and other key domains

  • What are the advantages of neural networks?

    Phew! There are a ton of advantages to learning neural networks in the scope of deep learning.
    ● Neural networks can handle a huge amount of data. This is something traditional machine learning algorithms struggle with
    ● Neural networks have a proven record of improving on the performance of traditional machine learning algorithms
    ● Neural networks can implicitly detect complex non-linear relationships between variables
    ● Wide range of applications! Neural networks are being used across industries, roles and functions

  • What kind of challenges could you face when applying neural networks?

    While neural networks like CNN and RNN are inherently awesome and are changing the deep learning field, there are still a few challenges you might face.
    ● Computation power: Deep neural networks like CNN and RNN can take up a lot of processing power
    ● Interpretability: Deep neural networks might be difficult to interpret or explain
    ● Overfitting: Modern neural networks have a tendency of overfitting on training data (but this can be rectified with regularization)
    ● Large datasets: Neural networks typically need large amount of data to train on

  • What exactly should you learn about neural networks?

    This is the million dollar question! What exactly should you learn about neural networks? That’s where this free course by Analytics Vidhya will help you.

    We will cover topics like what is a neural network, the different parts of a neural network, applications of a neural network, among other things. We have also baked in exercises after each video lesson to test your neural network knowledge!

  • What are the core concepts that make up a neural network?

    Excellent question! Here are three key concepts you should know about the workings of a neural network:
    ● What is forward propagation?
    ● What is backward propagation (backpropagation)?
    ● Role of Gradient Descent in neural networks
    We will cover each of these topics in the course.

  • What are the different types of neural networks in deep learning?

    There are different types of neural networks you will come across. Here are a few key ones:
    ● Artificial Neural Network (ANN)
    ● Recurrent Neural Network (RNN)
    ● Convolutional Neural Network (CNN)
    ● Long Short Term Memory (LSTM)
    We’ve seen CNN and RNN getting a lot of attention lately in the deep learning community.

  • Which programming language should I learn to build neural networks?

    Python is the preferred language for learning and building neural networks in deep learning. We’ve seen a strong preference towards Python and hence we use it in the course.

    If you’re new to Python, we recommend taking our free Python course.

  • What kind of problems or projects can I solve using neural networks?

    Neural networks open up a whole new world of projects. Once you have a hang of how neural networks work, you can get your hands on a dataset and start solving problems right away. We recommend heading over to our DataHack Platform and picking up the problem that you find the most interesting or relevant.

Course curriculum

  • 1
    Introduction to Deep Learning
    • What is Deep Learning?
    • Difference b/w Deep Learning and Machine Learning
    • Why Deep Learning is so popular?
  • 2
    Getting ready for the course
    • Hardware for Computations in Deep Learning
    • Setting up your system
    • Introduction to Google Colab
    • Understanding Google Colab Interface
    • Pre-requisites for Deep Learning
  • 3
    Introduction to Neural Network
    • Perceptron
    • Quiz - Perceptron
    • Weights in Perceptron
    • Quiz - Weights in Perceptron
    • Multi Layer Perceptron
    • Quiz - Multi Layer Perceptron
    • Forward and Backward Prop Intuition
    • Quiz - Forward and Backward Prop Intuition
    • Gradient Descent Algorithm
    • Quiz - Gradient Descent Algorithm
  • 4
    Activation Functions
    • Why do we need activation functions?
    • Quiz - Why do need activation functions
    • Linear Activation Function
    • Quiz - Linear Activation Function
    • Sigmoid and tanh
    • Quiz - Sigmoid and tanh
    • Softmax
    • Quiz - Softmax
  • 5
    Loss Function
    • Introduction to loss function
    • Quiz - Introduction to Loss Function
    • Binary and Categorical Cross entropy / log loss
    • Quiz - Binary and Categorical cross entropy / log loss
  • 6
    NN on structured Data
    • Understanding Problem Statement: Loan Prediction
    • Data Preprocessing: Loan Prediction
    • Quiz - Data Preprocessing: Loan Prediction
    • Steps to solve Loan Prediction Challenge
    • Loading loan prediction dataset
    • Defining the Model Architecture for loan prediction problem
    • Training and Evaluating model on Loan Prediction Challenge
    • Quiz - Training and Evaluating model on Loan Prediction Challenge
  • 7
    Assignment: Big Mart Sales Prediction
    • Assignment: Big Mart Sales Prediction
  • 8
    Real World Use cases of Deep Learning
    • Object Detection, segmentation, image generation
    • Quiz - Object Detection, Segmentation, image generation
    • Sequential Modeling
    • Quiz - Sequential Modeling
  • 9
    Where to go from here?
    • Where to go from here?

Instructor(s)

  • Aishwarya Singh

    Aishwarya Singh

    Aishwarya is currently working as a Data Scientist at Analytics Vidhya. She is one of the primary content curators and an instructor for Analytics Vidhya’s most popular course – Applied Machine Learning. She is also an avid reader and blogger who loves exploring the endless world of data science and artificial intelligence. She has written over 70 articles in recent years on various machine learning and deep learning topics and applications.
  • Kunal Jain

    Founder & CEO

    Kunal Jain

    Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
  • Pulkit Sharma

    Pulkit Sharma

    Pulkit is a Data Scientist at Analytics Vidhya. His research area lies in the field of Computer Vision and Deep Learning. He has been working on various projects related to images and videos for the past few years. He is comfortable with Python, Keras, PyTorch and has done multiple projects using these frameworks and tools. Some of his key projects include Crowd Counting, Estimating the Screen Time in videos, Object Detection, and Image Segmentation. He is one of the primary content curators for Analytics Vidhya’s courses, such as the Computer Vision using Deep Learning and Applied Machine Learning. He is also an avid blogger and has written multiple detailed and in-depth guides on various computer vision topics and applications, ranging from Image Classification to Object Detection and Image Segmentation.

Here's what our students say about Getting Started with Neural Networks

  • Short and simple

    Satish Jaiswal

    This free course was Awesome! I had no idea what Neural Network was but now I have thanks to Analytics Vidhya! You guys at the back-end of this course rock!

    This free course was Awesome! I had no idea what Neural Network was but now I have thanks to Analytics Vidhya! You guys at the back-end of this course rock!

    Read Less
  • Awesome Course

    deepakshar211.analyticsvidhya deepakshar211.analyticsvidhya

    Best course I have ever completed in such a short time

    Best course I have ever completed in such a short time

    Read Less
  • 5 star for back propagation

    Dhiraj Patil

    must see back propagation tut.

    must see back propagation tut.

    Read Less
  • Intro to Neural Networks

    sukanthen ss

    This course proves to be a good guide for basic knowledge on neural networks.

    This course proves to be a good guide for basic knowledge on neural networks.

    Read Less
  • really helpful

    Monika Varshney

    please provide more mcqs and calculative questions on neural networks..thanks..nice explanation..expecting in more detail so that we get overall idea about it

    please provide more mcqs and calculative questions on neural networks..thanks..nice explanation..expecting in more detail so that we get overall idea about it

    Read Less
  • Awesome

    Arghyadeep Sen

    Great introductory video with clarity in concept

    Great introductory video with clarity in concept

    Read Less

Frequently Asked Questions

Common questions related to the Introduction to Neural Networks course.

  • Who should take this course?

    This course is for people who want to kick start their deep learning journey and learn what and how neural networks work.

  • I have a programming experience of 2+ years, but no background in Deep Learning. Is the course right for me?

    You’ve arrived at the perfect place! This course is ideal for beginners in deep learning. You will learn neural networks from scratch, the building block of deep learning.

  • What is the fee for this course?

    This course is free of cost!

  • How long would I have access to the “Introduction to Neural Networks” course?

    Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost.

  • How much effort will this course take?

    You can complete the "Introduction to Neural Networks" course in a few hours. You are also expected to apply your knowledge of neural networks and learning of this course to solve deep learning problems. The time taken in projects varies from person to person.

  • How can I apply and test my learnings about Neural Networks?

    You can start by doing the tests at the end of the lessons in this course. In addition, you can apply Neural Networks to solve various problems on Analytics Vidhya’s DataHack Platform.

  • I’ve completed this course already and have a decent knowledge about neural networks. What should I learn next?

    That’s great! We recommend taking the next step in your deep learning journey with the popular “Computer Vision using Deep Learning” course. You will work on real world hands-on computer vision case studies, learn the fundamentals of deep learning, and get familiar with tips and tricks to improve your deep learning models.

  • . Can I download videos from this course?

    We regularly update the "Introduction to Neural Networks" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.

  • Which programming language is used to teach Neural Networks in this course?

    This course uses Python programming language and its open source libraries to teach you how to build neural networks.

Enroll in Getting Started with Neural Networks today

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