Udacity’s Deep Learning Nanodegree has been created precisely for students passionate about AI or deep learning, and basic knowledge about programming. It’s a beginner-friendly and reliable program.
In this Udacity Deep Learning Nanodegree review, I will talk about its syllabus in depth along with some pros and cons.
Udacity is offering personalized discount.
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This is my honest review of Udacity’s Deep Learning Nanodegree.
Let’s get started.
At first, I will make a quick walkthrough of Nanodegree programs.
If you don’t know much about Udacity, have a look at the detailed Nanodegree review.
Last year, I grew a passion for data science and decided to build upon my current skills as a computer engineer and pursue a career in it, I decided to learn each individual skill separately. My first step was to draw a roadmap after a lot of research that started by taking the Data Analyst Nanodegree which I completed 4 months before enrolling in the Deep Learning Nanodegree. My experience in the Data Analyst Nanodegree is probably the most important reason why I decided to learn Deep learning from Udacity as it was really great. At the start I was asking the same questions as you: will the Deep Learning Nanodegree be a disappointment? or will it exceed my expectations? perhaps it will be just fine? Fortunately, I got it as a scholarship, so I’m here to answer these questions for you to end your confusion.
Table of Contents
About The Udacity Deep Learning Nanodegree
When I started the program, I was supposed to set up deadlines for all the projects. It gave me an overall idea to complete the course within a stipulated time.
You can change the deadlines later as per your convenience.
Though I missed some of the deadlines, I hustled more on weekends to complete the course.
It gave me some idea about the pace at which I should finish the Nanodegree.
Here’s how it looks.
They gave me a unique chart to create a study plan. I did not use it much. However, I made my study plan with pen and paper. I do recommend you to use this feature.
The sidebar is quite handy to move between chapters. I was quickly able to go back and forth between chapters.
Once you are in the middle of the lesson, the sidebar shows you how much percentage of the study you have completed.
Udacity’s Teaching Style
I loved their teaching style. You get a 2-3 minutes video lecture, not more than that, or else people like me fall asleep, and then they throw you with some quizzes from the video.
These quizzes are in the form of Fill in the blanks or True or False.
They make sure you have understood the video properly.
In each of the main topics, there’s a new tutor. It allowed me to see different teaching styles as each instructor came from a diverse background and expertise. They also shared their stories.
Cost and Duration of Deep Learning Nanodegree
You can become a skilled Deep learning Engineer with Udacity by investing $339 per month for four months, and if you pay in lump sum fee for 4 months, Udacity will offer you a massive discount at the end of the degree.
I honestly believe Udacity is expensive, but if you get about 50% of 70% off on the course, get in.
Now let’s shed some light on the syllabus in this Udacity Deep Learning Nanodegree Review.
The course structure was excellent, compared to Coursera’s famous Deep Learning Specialization, the Deep Learning Nanodegree covers more topics that are arguably vital in the field of machine and deep learning.
Module 1: Introduction
This part aims at getting you comfortable with the workspace environment you will be working on which is called Jupyter Notebook, it teaches you how to install and manage it through Anaconda, glances over some fascinating applications of deep learning to get you excited, followed by a quick refresher on NumPy and basic linear algebra. This was an easy and short part; therefore, you won’t probably have any issues going through all its lessons in one or two study sessions.
Module 2: Neural Networks
The explanation in this module was exceptional, I got a deep understanding of the math behind neural networks (which are the main building blocks of deep learning) and the process in which the neural network learns by that’s called backpropagation, as well as an introduction to arguably the best Python framework for deep learning research which is called PyTorch. this was a very challenging module, and most of the quizzes were relatively hard to solve. You will probably struggle if you don’t have an above intermediate mathematical background. In my honest opinion, I think this is the most challenging part of the Nanodegree. If you were able to understand every idea in this module, it will make the rest of the Nanodegree much easier, so I suggest you invest a lot of time and effort in it if you decided to take the Nanodegree.
Project 1: Predicting Bike-Sharing Patterns
In this project, you will build your first neural network which will be a very straightforward task if you understood the lessons and solved the practice quizzes. In all projects, you will be guided in each step through a Jupyter Notebook. you won’t have to write all lines of code yourself, however, you will build the neural network from scratch, which means you won’t use PyTorch to make it. I think this is great because it will help you develop a deep understanding of the mechanism of the deep learning process.
Module 3: Convolutional Neural Networks
This is arguably the most fun part of the Nanodegree. The lessons were very well structured, and the instructors went through all-new concepts in much detail. CNN’s are mostly used in computer vision applications, and this module provides a perfect introduction to the concepts and applications of CNNs. As training the CNNs is very computationally expensive and heavy, you will get introduced to the world of cloud computing as this will probably be the way to go for training your deep learning models. And it’s not only explanation videos, but you will also walk through 3 CNN’s projects in a perfect way, which means you get a brief idea about the new concept or application and then you walk through a Jupyter Notebook to apply what you have just learned in much detail, and with every small task being solved and explained after, this is certainly the best method to learn and one of the best parts about the Nanodegree
Project 2: Dog-Breed Classifier
Classifying different dog breeds is a difficult task for humans, this is where deep learning comes in handy. You will build a CNN that can differentiate and identify 133 different dog breeds with high accuracy, this will show you the potential of deep learning and the context of the project will be within the development of a mobile application with this purpose. This project was really challenging yet very fun. This is a sample random image I got from the internet and gave to CNN and it was able to classify it correctly.
Module 4: Recurrent Neural Networks
This module is very challenging and needs a lot of time to let the new information sink in, however, it’s very well structured and it even begins with a review on the ideas from the second module and then builds upon it, the lessons are well structured and there are many instructors that are good at explaining and simplifying difficult things, especially Luis Serrano. For me, this was the most important part of the Nanodegree as RNNs are best used in natural language processing applications which are very widely used in data science, I was looking up for it from the start, and it didn’t disappoint.
Project 3: Generate TV scripts
The RNN you will build in this project will learn from a TV show script file to generate its own script, amazing right? Well not exactly, let me show you why. This image shows the output of the RNN, and it’s really fascinating that a machine could learn to generate text on its own, however, it’s not making any sense although there are somewhat complete sentences, this will show you the limitation of deep learning and why that getting good results could take a lot of developing and training both being equally important.
Lesson 5: Generative Adversarial Networks
Things were going great thus far, and I was excited to learn about GANs which are basically two neural networks working together. The first three lessons were great, the fourth lesson on CycleGANs was a disaster, the worst lesson in the whole Nanodegree, the instructor is one of the creators of the latter, however, I couldn’t understand literally anything due to his accent which was extremely hard to understand, even with closed captions turned on, it wouldn’t recognize most of the words. I went through all lessons twice and couldn’t get most anything. The next lesson was how to implement the CycleGANs going through a Jupyter Notebook with a different instructor, however, this lesson builds upon the previous and won’t be enough on its own to understand the concept, at least, this was the case for me.
Project 4: Generate Faces
In this project you will build an advanced neural network called a deep convolutional generative adversarial network, I know it sounds intimidating, but it was fun to make and the idea behind it is very intuitive. The downside of this project is that it’s mostly replicated in the second lesson of the module. This means you will do almost the exact same project twice. I think it would’ve been better if they’ve chosen a different application or even a different approach.
Module 6: Deploying a Sentiment Analysis Model
This is one of the easiest, yet most important modules of the Nanodegree, as model deployment is one of the vital parts of any deep learning pipeline. The first lesson introduces you to the machine and deep learning workflow, the rest of the lessons will guide you through each step and will teach you how to deploy models on AWS, you will get a great explanation of each step. This part is arguably the most code-based part of the Nanodegree, meaning that you will take what you have just learned and apply it to deployment. which (in my opinion) is the best cherry on top for the Nanodegree.
Project 5: Deploying a Sentiment Analysis Model
This project focuses on the deployment, so you will be provided the RNN in a python file, and your task will be to get it deployed on AWS so that everyone can use it through a simple web app which will also be provided. The project was well guided and straightforward, but you will need to have an AWS working account, and if you are in the free tier you will need to request a proper GPU instance for training freely, otherwise, you won’t be able to complete the project. It usually takes 2-3 days for AWS to approve the request, so keep this in mind.
Also Read: Udacity Machine Learning Nanodegree review
How was your project experience
The projects were all very well structured and guided. They are arguably the #1 best thing about Udacity Nanodegrees, they are meant to get you involved to boost your confidence in what you’ve just learned, moreover, they are real-world applications and will complete your practical understating of each concept.
My thoughts on course pricing
I got the Nanodegree as a scholarship, generally, I think Udacity Nanodegrees are relatively overpriced. Its normal price would be a 4-month subscription which is 1356$.
My thoughts on the course timeline
The recommended timeline for the Nanodegree is 4 months, you can subscribe for as many months as you want, however, if you choose the recommended timeline, you will get a 15% discount. For me, I think 3 months will be more than enough, but if you have a lot going on in your life, I recommend that you subscribe for the 4 months timeline. Let me tell you a clever way to complete it with the least fees possible:
- First, you audit the deep learning specialization offered from Coursera and you complete all 5 courses which almost cover about 65% of the deep learning nanodegree, as you won’t learn there about GANs and model deployment, this step is totally free and legal, however, the quizzes will be locked, and you won’t receive a certificate of completion. but you could open the quizzes and get a certificate of completion by purchasing the specialization, which is significantly cheaper than Udacity, or applying for a financial aid.
- Second, you apply for a personal discount from Udacity which can give you about 75% off, and then you subscribe for 1 month which will be more than enough to study the parts you need and finish all projects if you put the time and effort for it (choose a relatively free month for you), this way you get the nanodegree for about 100$ which is great.
Thoughts on some Udacity features like
A great feature, but normal to have. I didn’t need to use it.
2. Project reviews:
Project reviews were mixed, this is because project reviewers vary, some projects were reviewed in more detail than others, it’s clear when the reviewer pays attention to the smallest details and gives you really great advice on your implementation.
3. Career services:
This is one of the best services of the Nanodegrees, you get 4 additional lessons that teach you how to build a portfolio on GitHub, how to set up and use your profile on LinkedIn, how to make a good resume, and finally how to write a cover letter, but that’s not the cool part, it’s that after each lesson you will provide what you made or the link to it to an expert for feedback, much like a project, but it’s optional.
What you liked about Udacity (Pros):
- Their focus on learning by doing methodology.
- The structure of the curriculum is excellent.
- Every new concept is nicely separated in a video followed by a quiz.
- The projects are real world applications (not theoretical).
- The content is very top quality (almost all of it).
What you didn’t liked about Udacity (Cons):
- The GANs module isn’t well developed.
- Some projects were not reviewed in detail as the others.
- The original pricing is very expensive.
Impact of Deep Learning Nanodegree on my life
For me, this Nanodegree proved to be a great opportunity. It deepened my understanding of deep learning as it was also the topic for my thesis.
Specifically, what I learned from this Nanodegree is how to use PyTorch. For me, it was a new framework. Later I took it in my thesis.
Although I would say they did not cover everything on PyTorch, I had to study a lot by myself. Overall it was a good start.
One more thing I would like to share. I think I started appearing in many LinkedIn searches, and then recruiters started to contact me. When I was going to job interviews, it gave me something to talk about. So I talked a lot about projects and how I can make work on real-life ideas.
Udacity also checked my CV, Linkedin, and Github account and suggested improvements. It’s a part of their career services.
At the same time, this course gave me a bit of stress as I managed multiple things at a time(including a full-time job). So I think if I could do it, you too can.
Conclusion: Do you recommend it to others?
I extremely recommend this Nanodegree to anyone pursuing a career in deep learning or data science.
Downsides of the course
I think Udacity should work on reducing the cost. It’s pretty expensive. I cannot pay $1200 for this course. If they are running a good discount, then only consider enrolling in it.
The second perspective is time. This course needs the commitment to complete it in the stipulated timeframe.
In some cases, it might push you as you have deadlines.
In my case, it was a scholarship, and I could not afford to lose this opportunity.
I had exams and my master’s thesis to complete. You can imagine the amount of pressure. I had to work day and night to finish this nanodegree.
Also, having a Nanodegree does not guarantee a job, but it gets you closer to having one.
Reviews from other graduates of Udacity Deep Learning Nanodegree
Arnab Ghosh Chowdhury
An interesting and challenging journey has come to end. I am really happy that I have received the certificate now. Many thanks to Udacity for giving me this opportunity.
Jonathan Benavides Vallejo
Software Engineer iOS – AI enthusiast
I found Udacity stood-up amongst every platform, it offers a great community, real-world examples, private coaching, career services, every project is reviewed by a real person, full flexibility whilst you are having a full-time job. Here is a list of my projects:
- Predicting bike-sharing data
- Dog bred classifier
- Face generation
- TV Script generation
- Amazon Sagemaker deploying sentiment analysis
The demand for Deep learning is increasing day by day due to its primary function of image and voice detection, so for that purpose, there is an emerging demand for deep learner researchers.
It was recorded in 2020 that a deep learner engineer earns an average income of $81,189 annually.
The demand for Deep learner engineers has been predicted to increase by 16% from 2018 to 2028.
Some extra perks of Udacity Nanodegrees
Now let’s discuss some benefits and features in this Udacity Deep Learning Nanodegree Review.
You can get personalized feedback from the network of 900 above project viewers. Moreover, the feedback on your projects is unlimited. It means these projects are not ordinary, but they will add up in your profile as professional projects.
At Udacity you will be provided by a personal counselor or a trainer who will help you throughout your course but only if you have paid for the deep learning Nano degree course.
At Udacity you are not only taught about the skills to learn in-depth learning Nano degree course, but they will also groom your professional skills.
They will give you advice and help you prepare for job interviews; besides, they will also give you some professional tips in making Cvs and Resumes provide a professional look.
Along with it, Guidance will also help you in sending your Cvs to the highly esteemed companies with which Professionals have done partnerships.
So you will also have a chance of getting a highly paid job in a well-recognized company which can give a kick start to your successful career.
Udacity also provides you with discussion forums to discuss your queries about the respective lecture with your class fellows.
This helps create a class-like atmosphere and enables you to explore more creative minds as you discuss.
Pros and Cons
So after discussing all the prerequisites, costs, duration, and syllabus of the Udacity Deep Learning Nanodegree in this Udacity Deep Learning Nanodegree Review. Now, let’s discuss some of the Pros and Cons of it.
Pros of the Udacity Deep Learning Nanodegree:
- The portfolios of the graduates of Udacity are shared with the partner companies that include Google.
- The certificate of completion by Udacity is highly recognizable.
- Your mentor personally review the projects.
Cons of the Udacity Deep Learning Nanodegree:
- Nanodegrees are bit expensive.(solution is getting a discount or apply for scholarship)
- This course requires your full attention, and you have to work very hard and complete several tiring projects to get a certificate.
We have given all the essential information and have answered all the queries regarding it the course and why you should choose the Udacity deep learning Nano degree course.
The Final Verdict: Deep learning Nanodegree Review
With a good investment, you can enjoy the virtual class, a healthy discussion forum, a personal trainer, and globally recognized certificates.
So that’s why we are signifying the importance of doing deep learning Nano degree courses in Udacity, which in return will make full use of your investment, and you won’t regret your decision.
Udacity is offering personalized discount.
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You May Also Interested In
Is Udacity Deep Learning Nanodegree worth it?
This course teaches you to create your own neural network coupled with some awesome projects. The average rating for this Nanodegree is 4.6/5. Hence I think it’s worth a try.
Deep Learning Nanodegree vs Machine Learning Nanodegree
Both Nanodegrees are best to embark on your journey of Artificial Intelligence, however, Deep learning Nanodegree focuses more on deep neural networks.
How good are the Deep learning Nanodegree instructors?
Most of the instructors come from prominent organizations like Google. For instance, Luis Serrano was a former engineer at Google. Other instructors are Mat Leonard(Physicist), Cezanne Camacho(Stanford University), Alexis Cook(Masters in computer science).
Can I complete Udacity Deep Learning Nanodegree in a month?
Yes, you can, provided you have intermediate knowledge of creating deep learning neural networks.
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