Deep Learning Course is one of the most pursued courses on Udacity, with a total duration of 4 months. Through this course students will have a thorough knowledge in deep learning fundamentals that would eventually help them launch or advance their career in AI and Machine Learning. The course currently can be availed at a price of INR 22,849 per month. Students who opt for a complete payment can avail this course at a discounted rate of 15% i.e INR 77,676 instead of INR 91,396.
The course discusses in detail the various intricacies of AI and Machine Learning, such as Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks and Generative Adversarial Networks, along with a flexible learning program schedule.
Learning Outcomes
- You will learn the fundamental techniques that support contemporary deep learning
- You can explore the neural network design constructs and the effects of these choices
- You will learn how to enhance accuracy and robustness of neural network training
- Learn the principal features of CNNs that distinguish them from regular neural networks for image processing
- You will discover the utilization of CNNs for semantic segmentation and object detection
- Explore various RNN architectures design patterns for those models
- Learn about generative adversarial networks (GANs) and process to construct and train various GAN designs to produce new images.
Course Highlights
| Key Highlights | Details |
|---|---|
| Price | INR 22,849 per month INR 77,676 ( |
| Duration | 160 Hours |
| Rating | 4.7/5 |
| Instructor | Erick Galinkin & Giacomo Vianello |
| Topics Covered | Introduction to Neural Networks, CNN Concepts, CNNs in depth, Autoencoders, Limitations of RNNs, Modern GANs |
| Course Level | Intermediate |
| Total Students Review | 3,367 |
| Merits |
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| Demerits |
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Course Content
| Sl. No. | Modules | Topics |
|---|---|---|
| 1 | Introduction to Deep Learning | Deep Learning |
| Minimizing the Error Function with Gradient Descent | ||
| Introduction to Neural Networks | ||
| Training Neural Networks | ||
| 2 | Convolutional Neural Networks | Introduction to CNNs |
| CNN Concepts | ||
| CNNs in Depth | ||
| Transfer Learning | ||
| Autoencoders | ||
| Object Detection and Segmentation | ||
| 3 | RNNs & Transformers | Recurrent Neural Networks |
| Long Short-Term Memory Networks (LSTMs) | ||
| Implementation of RNN and LSTMs | ||
| Fine Tuning RNN Models | ||
| Seq2Seq Architecture | ||
| The Limitations of RNNs | ||
| 4 | Building Generative Adversarial Networks | Generative Adversarial Networks |
| Training a Deep Convolutional GANs | ||
| Image to Image Translation | ||
| Modern GANs |
Resources Required
- Intermediate Python Knowledge
- Linear Algebra
- Derivatives
- NumPy Pandas
- Jupyter Notebooks
Comparison Table
| Parameters | Deep Learning | Computer Vision | Natural Language Processing |
|---|---|---|---|
| Offers | INR 22,849 per month INR 77,676 ( |
INR 22,849 per month INR 58,257 ( |
INR 22,849 per month INR 58,257 ( |
| Duration | 160 Hours | 120 Hours | 120 Hours |
| Ratings | 4.7/5 | 4.7/5 | 4.7/5 |
| Students Enrollments | - | - | - |
| Instructors | Erick Galinkin & Giacomo Vianello | Sebastian Thrun | Luis Serrano, Arpan Chakraborty |
| Level | Intermediate | Intermediate | Intermediate |
| Topics Covered | Introduction to Neural Networks, CNN Concepts, CNNs in depth, Autoencoders, Limitations of RNNs, Modern GANs | Convolutional NN Layers, Image Segmentation, Recurrent Neural Networks, Attention Mechanisms, Robot Localization | Text Processing, Deep Learning Attention, Modeling, Alexa History Skill, Information Systems |
| Projects | Yes | Yes | Yes |
| Other Similar Courses | Best Deep Learning Courses on Udemy | Deep Learning with Pytorch | |
| Python Courses | Java Courses | SQL Courses | |
| Machine Learning Courses | Artificial Intelligence Courses | Ethical Hacking Courses | |
Student Reviews
Listed below are some of the student reviews for Deep Learning Courses by Udacity.
- Vyom S. (5.0/5) “They've managed to cover almost the entire breadth of topics related to deep learning and neural networks. Earlier weeks focus on building up the fundamentals. Coming up to the last few weeks, I was already pretty comfortable with the topics. I especially loved the lessons they've built in collaboration with Ian Goodfellow and Andrew Trask.”
- Peter L. (5.0/5) “This is probably the most approachable way to get into deep learning I have found thus far. The course covers a lot of interesting subjects, with (usually) good explanatory videos and walkthroughs. These always feel fresh and get you motivated for the subjects you are about to learn. As a bonus, they have gotten a few known names to present individual subjects. As an example, the introduction to GANs is done by none other than the inventor himself, which is a cool bonus.”
- Mathiesha S. (5.0/5) “I love the Deep learning program, can't wait to get started with the rest of the projects. Thank you very much for the lectures and the content creators.”
- Amit U. (5.0/5) “I am about 30% into my DEEP LEARNING NANODEGREE. I am psyched now to complete my DOWNLOAD. At work, I get so excited that I want to go home and continue my CNN lectures or complete the models.It literally brings a smile to my face. Also, the study plan feature that texts and emails me is very very awesome. I stopped slacking since last week and now will start to work on DL projects previously done by others. Thank you Udacity team so so so much for putting the contents incredibly well.”
- Anup Joseph S (5.0/5) “It's excellent. The projects are really fun. It touches on a wide variety of neural network types and applications. This course would be a great starting point for someone with some ML experience but would like to learn more about Deep Learning. It's much more detailed than the other courses you would normally find online and doesn't shy away from the math, but at the same time it's not too theoretical. I often found the links that instructors leave for you to explore to be really useful.”
- Martin M. (5.0/5) “Even though I self taught python for myself, I’m doing really good until now. I’ve learned a lot of new stuff that will be useful to me for my future projects. Awesome work for teaching this content. Making it accessible for virtually anyone. I’m a Field Service Engineer and I can’t go to school right now because I’m always traveling. This is also not expensive at all and it’s a very good program for what it’s worth. Keep up the good work Udacity, you’re revolutionizing education to the next level.”
- Stephen E. (5.0/5) “My first project took much perseverance and going back through the videos helped. The cheat-sheet, unit test, and provision of the expected results were very helpful. I got a little overwhelmed at first with the project because of the real-world data but the pay-off was that I also got to really understand the dataset and then, through hand-calculations with the unit test data and the network steps, really got to understand the method and network architecture. Thanks for the journey so far.”
- Sanket G. (5.0/5) “Really good class with a great overview of all the architectures in deep learning and you'll appreciate the complexity also very well. I loved building NN from scratch all the way to using GAN to build your own faces. That was surreal! Some things they can improve are longer videos on CNNs and RNNs as they are quite complex, more focus on training and actual pre-processing work and using Keras as higher level APIs are what I would end up using. This course only focuses on TF. Overall, I loved it and highly recommended it!”
- Yifei P. (5.0/5) “The new Deep Learning Nanodegree Program is very good. It's much like an introductory course in graduate school but combines a lot of mini-labs and projects. To absorb the program well, you may need to spend at least 20 hours per week, not 13 hours per week. If you want to master deep learning, you may need to read the textbook written by Ian Goodfellow, which may take another 20 hours per week. Anyone who made you feel it's easy to break into AI (Artificial Intelligence) might have already harmed you!”
- Rishabh G. (5.0/5) “The program is a good mixture of challenges and great content. The instructors are really great. They don't spoon feed you the content. You have to get your hands dirty. What I really like is that the program forces you to study the topics from different sources and various exercises between the tutorials is a great way to apply the concepts so that you get a good grasp of them. Udacity really makes sure that the best way to learn is by doing. Thanks a lot for this great course.”
Deep Learning on Udacity: FAQs
Ques. Why should I enroll?
Ans. You will learn the principles of deep learning in this programme, that would help you to start or develop your career in AI. You will learn from industry professionals and acquire unique perspectives. This Nanodegree programme is a great place to start for anyone interested in developing expertise with this game-changing technology.
Ques. What jobs will this program prepare me for?
Ans. This course is intended to improve your deep learning abilities. As such, it not only prepares you for a particular job but simultaneously broadens your deep learning skills. These abilities can be used in a variety of contexts and make you eligible to pursue more studies in the area.
Ques. How do I know if this program is right for me?
Ans. If you are intrigued by the topics of this course, this Nanodegree programme is the ideal method to break into the artificial intelligence and machine learning industries.
Ques. Do I need to apply? What are the admission criteria?
Ans. There is no admission criteria. Candidates regardless of their experience and qualifications are eligible for admission for this programme.
Ques. What are the prerequisites for enrolment?
Ans. Learners for this program should have a well defined knowledge of the following subjects listed below,
- Intermediate Python
- Linear Algebra
- Derivatives
- NumPy, Pandas
- Jupyter Notebooks
Ques. If I don't meet the requirements to enroll, what should I do?
Ans. There are several other courses that can be taken up if you’re not fulfilling any prerequisites for this course. Some of the most relevant courses that can be taken up are listed below,
- Intro to Machine Learning with TensorFlow
- Introduction to Data Analysis
- Introduction to Python
- Linear Algebra Refresher
- Introduction to Programming
Ques. How is this Nanodegree program structured?
Ans. The content and curriculum in this Deep Learning Nanodegree programme support 4 projects and 4 courses. We estimate that students can finish the programme in 4 months if they work 10 hours each week. The Udacity reviewer network will examine each project. You'll receive feedback, and if the students do not clear the projects, they'll be asked to re-submit it until they clear it.
Ques. How long is this Nanodegree Program?
Ans. The entirety of this course runs for about 160 hours or 4 months.
Ques. I have graduated from the Deep Learning Nanodegree program, but I want to keep learning. Where should I go from here?
Ans. Post successfully completing the course, graduates from this Nanodegree programme are guaranteed admission into our more difficult Self-Driving Car Engineer or Flying Car Nanodegree programmes, provided they pay the associated tuition fees.
Ques. What software and versions will I need in this program?
Ans. The following softwares will be required for pursuing the program,
- NLTK
- SKLearn
- BeautifulSoup
- NumPy
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