• Time

    Foundation I: 2 months

    Foundation II: 4 months

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Why Take The Deep Learning Program?

Artificial Intelligence is transforming our world in dramatic and beneficial ways, and Deep Learning is powering the progress. Together with Siraj Raval, Udacity provides a dynamic introduction to this amazing field, using weekly videos, exclusive projects, and expert feedback and review to teach you the foundations of this future-shaping technology.

Siraj Raval

What You Will Learn

Foundation I

Learn how to use Anaconda and Jupyter for data analysis. Also, go through some key math concepts behind deep learning. Vincent Vanhoucke, Principal Scientist at Google Brain, introduces you to deep learning and Tensorflow, Google's deep learning framework. Learn how to create your first neural network.

Get introduced to neural networks and build your first basic neural network

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2 months to complete

Prerequisite Knowledge

Programming knowledge needed: Basic to intermediate Python, experience with Numpy, Anaconda, and Jupyter Notebooks.
Math knowledge needed: Algebra. Partial derivatives. Matrix multiplication.

  • Introduction to Deep Learning

    Learn about the program and explore the various applications of Deep Learning networks. Also learn how to use the tools and calculations that will be needed during the program with some introductory lessons.

  • Neural networks

    Understand the fundamentals of neural networks and build your first, using Python and Numpy. You'll also get an introduction to Tensor-Flow and learn how to use it to build deep neural networks.

    Icon project 96f52036b807dfc0ec7e1757eb0e07a56e11058e04e414a1578f10138f61c939 CREATE YOUR FIRST NEURAL NETWORK
  • Convolutional Neural Networks

    A few years ago, convolutional networks (CNNs) transformed the field of computer vision by enabling powerful imaging capabilities. In this lesson, you'll learn how to build CNNs and use them to sort images based on the objects displayed

    Icon project 96f52036b807dfc0ec7e1757eb0e07a56e11058e04e414a1578f10138f61c939 CLASSIFY IMAGES

Foundation II

Learn the concepts behind recurrent networks and see how a character-wise recurrent network is implemented in TensorFlow. Implement a sentiment prediction RNN and learn about a specific architecture of RNNs for generating one sequence from another sequence. Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.

Learn to create more advanced neural networks and learn how to classify images and generate faces

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4 months to complete

Prerequisite Knowledge

Basic knowledge of Neural networks. Preferable if you have built one already Programming knowledge needed: Basic to intermediate Python, experience with Numpy. Anaconda and Jupyter Notebooks
Math knowledge needed: Algebra. Partial derivatives. Matrix multiplication.

  • Recurrent Neural Networks

    Recurrent neural networks (RNNs) are able to find information about sequences in data such as the order of words in texts. RNNs are also great as an extraction feature for texts that can be used, for example, in the analysis of feelings. You will use a recurrent network to generate new texts and do language translations.

    Icon project 96f52036b807dfc0ec7e1757eb0e07a56e11058e04e414a1578f10138f61c939 GENERATE TV SCRIPTS Icon project 96f52036b807dfc0ec7e1757eb0e07a56e11058e04e414a1578f10138f61c939 TRANSLATE A LANGUAGE
  • Generator Networks

    Opposing Generation Networks (GANs) place two neural networks to compete with each other, allowing them to model reality with incredible accuracy. Ian Goodfellow, the inventor of the GANs, will show you how these fascinating models work and teach you how to build them.

    Icon project 96f52036b807dfc0ec7e1757eb0e07a56e11058e04e414a1578f10138f61c939 GENERATE FACES

Learn with the Best

Mat Leonard
Mat Leonard

Curriculum Lead

Mat, the Curriculum Lead, is a former physicist, neuroscientist, and data scientist with a passion for education. Recently, he led the Deep Learning Nanodegree Foundation program covering state-of-the-art machine learning models.

Siraj Raval
Siraj Raval

Instructor

Siraj is a Data Scientist, bestselling author, and YouTube star. He make videos that teach people how to use machine learning to create game bots, chatbots, self-driving cars, programs that create art and music, stock prediction models, and much more.

Choose Any Foundation Course

Deep Learning Foundation I
₹16,500
₹13,500*

TOTAL
7 days no questions asked refund available

Time to complete: 2 months

If you are new to deep learning or machine learning. Foundation I is not mandatory to enroll in Foundation II

PREREQUISITES

Prerequisites: Python programming. Maths concepts

START FOUNDATION I
Deep Learning Foundation II
₹19,500
₹16,500*

TOTAL
7 days no questions asked refund available

Time to complete: 4 months

It is recommended that you take Foundation I before taking Foundation II

PREREQUISITES

Python programming. Knowledge of basic neural networks

START FOUNDATION II

* Price exclusive of taxes. GST of 18% gets applied on checkout. Option to convert your payment into EMI available on checkout page.

All the above payment plans include the costs of Unlimited project reviews (by Udacity's global reviewer network), in-classroom mentorship support through chat with industry experts and Access to global community of Udacity students pursuing the same Nanodegree.

Deep Learning Foundation I

Course Fee: ₹2,500

GST(18% on deposit Fee): ₹450

Total amount to be paid: ₹2,950 for 2 months


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Deep Learning Foundation II

Course Fee: ₹3,500

GST(18% on deposit Fee): ₹630

Total amount to be paid: ₹4,130 for 4 months


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Payment Options

We support multiple payment options for your convenience. Choose an option that suits you the most

Credit/Debit

Credit/Debit Card

Accepting payments through Credit/Debit cards from all major banks.

Emi

EMI on Credit Card

Split the payment of the course fee across 3 or more months. Option is available for all plans. Instalment information available on the payment page.

Internet Banking

Internet Banking

Seamless payment though Internet banking accepting payments from over 59 banks.

Features 1
Amazing content & live sessions

Amazing Content, Exclusive Access

Concept mastery with Udacity experts, code walkthroughs with Siraj Raval, exclusive lessons with Andrew Trask and Ian Goodfellow, and more!

Projects with expert feedback

Unique Projects, Expert Feedback

Work on five specially-designed Deep Learning projects, and benefit from expert feedback on each.

Features 2
Guaranteed Admission

Guaranteed Admission

Successfully complete the program, and receive guaranteed admission to our Self-Driving Car, Artificial Intelligence, or Robotics Nanodegree programs!

Earn a Udacity Foundation Nanodegree

Earn your Udacity Nanodegree Foundation credential

Every program graduate receives a Udacity credential affirming their mastery of Deep Learning foundations.

“Deep Learning is a transformational technology that we see around us every day in medical imaging, Google searches, self-driving cars, and more. We are just at the start of what this technology can do for us, and I can't wait to see what our students build next.”

— SEBASTIAN THRUN Founder, Udacity

Student Reviews

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Siraj's Deep Learning FAQ

  • What is a “Nanodegree Foundation program,” and how does it differ from your existing Nanodegree programs?

    A Nanodegree Foundation program is designed to facilitate your entry into a particular arena of study, with the goal of ensuring that you establish your “foundations” in the field. Depending on your longer-term goals, a Foundation program can enable you to enhance an existing skillset, move forward into deeper and/or more advanced academic studies, or prepare for a career move that requires a fuller understanding of certain technologies and concepts.

    See More Questions

START BUILDING YOUR DEEP LEARNING FOUNDATIONS TODAY

Enroll today, and start putting your skills to work!

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Data Analyst Nanodegree

Course Fee : ₹3,500

GST (18% on course fee): ₹630

Total amount to be paid: ₹4,130

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