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Udacity Deep Learning Nanodegree Review- Is it worth it?

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Udacity Deep Learning Nanodegree Review- Is it worth it in 2022?

Deep Learning is a powerful tool that can be used to solve complex problems. It relies on artificial intelligence (AI) to make predictions about what will happen next. Deep Learning can be used for a variety of tasks, including natural language processing (NLP), image recognition, and fraud detection.

Deep learning is a branch of machine learning that involves using large memory and processing power to perform complex tasks automatically. Deep learning has the potential to revolutionize many industries, especially if it can be applied in a way that is both scalable and efficient.

Join the next generation of AI talent that will help define a highly beneficial future for our world. In this Nanodegree program, you’ll study cutting-edge topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks.

About Udacity:

Udacity is an online company that provides a variety of online courses that cover various educational topics. It was founded by Sebastian Thrun, David Stavens, and Mike Sokolsky. Udacity was started in 2011 with free computer science classes at Stanford University. Udacity offers a range of courses, such as free and paid courses that require online certification, such as Nanodegree programs.

Some Features of the Udacity Nanodegree Program :

Some of the unique features you will find at Udacity cannot be found anywhere else. These unique features are what actually make Udacity one of the very best platforms by which you can enroll in an online course.

  • Real-world projects from top industry experts

With real-world projects and engaging content created in collaboration with top-tier firms, you’ll master the IT skills that employers demand.

  • Technical Support by mentors at Udacity

The Smart and knowledgeable mentors at Udacity will guide your learning and are always available to answer your questions, help you and keep you on track

  • Career services

You’ll have access to GitHub portfolio reviews and LinkedIn profile optimization to help you develop your career and obtain a high-paying position.

  • Learn with your own freedom

Create a learning plan that matches your busy schedule. Learn at your own speed and on your own timetable to achieve your specific goals.

Program offerings

Class content – Real-world projects, Project reviews, and Project feedback from experienced reviewers

Student services – Technical mentor support, Student Community

Career services – Github review, Linkedin profile optimization

Meet Your Instructors
  1. Erick Galinkin – Principal AI Researcher | Rapid7
  2. Giacomo Vianello – Principal Data Scientist
  3. Nathan Klarer – Head of ML & COO of Datyra
  4. Thomas Hossler – Sr Deep Learning Engineer

Requirements for this course:

To be successful in this course you should have familiarity with topics like Intermediate Python, Linear Algebra, Derivatives, Numpy, Pandas, and Jupyter notebooks

What you will learn in this Course – Course Breakdown

Now let’s come to the most important part of the course which is the course itself and what you get in it when you enroll in this course. This Course has a total of four sections each explaining some important topics related to the course and providing with you learning points and real-world projects at the end. Let’s take a deep dive into the sections -:

Introduction to Deep Learning

First things first. Learn about the foundations of modern deep learning, working with gradient descent and backpropagation algorithms. Next, explore how neural networks are designed, as well as the impact these design choices have on training. Finally, the course describes methods of optimizing neural network training for accuracy.

In this machine-learning project, you will develop a handwritten digit recognition function in PyTorch. Then, use data pre-processing skills to load the necessary data for use in models. Develop a neural network using PyTorch and write a loop to train the model using the loaded data. Lastly, employ advanced training methods to further improve accuracy on the training set.

Convolutional Neural Networks

Convolutional neural networks, or CNNs, are the most widely used kind of neural network for image recognition. In this section, you will learn the main differences between CNNs and standard neural networks, and discover how CNNs can apply to custom datasets using transfer learning.

In this project, you will create programs to automatically determine the spot of an image by analyzing any landmarks that appear in it. You will proceed with machine learning development from beginning to end, perform preprocessing, design and build CNNs, compare the accuracy of different CNNs, and develop a smartphone app using the finest CNN you designed.

RNNs & Transformers

In this section, numerous RNN architectures are introduced, and data structures and algorithms are explained in detail for regular and recurrent neural networks. In addition, the transformer-RNN architecture is discussed, and you’ll discover several applications for this technology in specific scenarios.

In this project, Build an AI chatbot for replicating a conversation using LSTMs, Seq2Seq, and word embeddings using the conversational ability dataset in this project. Here’s how to use Pytorch to produce an artificial neural network architecture, train it with conversational ability data, and tune the neural network’s hyperparameters.

Building Generative Adversarial Networks

In this section, Learn to build and train generative adversarial networks (GANs) in order to develop brand-new images. Build, train, and research architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on distinct datasets, including the MNIST dataset, Summer2Winter Yosemite dataset, or the CelebA dataset.

In this project, Build, train, and research architectures such as CycleGAN, DCGAN, ProGAN, and StyleGAN in order to build brand-new images. Use these architectures on the MNIST dataset, the Summer2Winter Yosemite dataset, or the CelebA dataset.

How long will it take to finish the course?

According to the Program, the course is expected to be completed within approximately 4 months if you devote a minimum of 10 hours per week to the course. As we mentioned above, they have a self-paced learning environment, so you can attend at your discretion and at your pace.

If you take more than 4 months to finish the course, you have to take the monthly pay-as-you-go plan and pay extra which will increase your overall cost of the course.

What’s the Cost of the Course

Now let’s talk about the cost of the course which is an important part of whether you will buy or not buy the course. In this course, Either you will pay for monthly access or you can also choose a 4-Months access plan.

If you choose the monthly pay-as-you-go option you will pay $399 per month and there is another option that you can choose which comes with exclusive discounts which is a 4 months plan that you need to pay upfront and costs you around $1356 which comes with exclusive discounts making it cheaper than the monthly plan and also recommended by Udacity.

If you pay upfront for the 4 months’ access you can save up to 15% + 70% exclusive discounts which you cannot if you take the monthly plan. If you need more time after 4 months, you can switch to a monthly access plan but it will increase the overall cost of the course.

Exclusive Discounts

Udacity will give you personalized Discounts if you answer 2 questions and pay upfront rather than a pay-as-you-go plan. You will get a promo code with an exclusive 70% Discount just for you on your course by just answering 2 simple questions.

What Other Students have to say about the Course

While looking at ratings and reviews of this course, One has to say that this course is very popular among the students with an overall rating of 4.7 out of 5 stars, and many good quality reviews are given by already enrolled students in the courses. Some of the reviews are -:

“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.”

-Peter L.

“I couldn’t be more happy with my experience. Udacity has changed my life, and I expect more changes as I will pursue another Nanodegree. I must confess this program has been extremely challenging. I have had to overcome certain aspects of myself, fears and insecurities, to be able to finalize this Nanodegree satisfactorily.

Thus, in some way, I could say that this experience has helped me to become a better person, making me release a better version of myself. In order to be able to complete it, I have had to change the way I used to study.

I have had to question many aspects of myself, and to reengineer on some points of my learning process. After all this process, I can say that my mindset has definitely changed. On the other hand, it wouldn’t be for me so easy to start a new career path in AI, or at least, not so quickly.

– Nohemy V.

Demand/Jobs for this role:

Deep learning is a branch of machine learning based on a set of algorithms that attempts to model high-level abstractions in data. It’s a data-driven approach to artificial intelligence inspired by the brain’s ability to learn. Deep learning is used in many fields, including computer vision, speech recognition, natural language processing, and bioinformatics.

The demand for deep learning is increasing as the need for artificial intelligence grows. The salary for deep learning experts varies depending on experience and location, but the average salary is around $100,000.

Final Thoughts

This is overall a good course created by experts at Udacity and also the features and offers provided by Udacity make this course a very good Nanodegree program. Also, You should also check other courses which you can take right after this course as those courses are made with help of top tech companies, they are of high quality and make you more knowledgeable about your field. If you are interested in other Udacity courses, please check out all courses on our website.

The course also has easy to follow a curriculum that includes everything to build your foundation. And also every section at the end includes a real-world project that will give you practical experience and make you job-ready.

One thing you should keep an eye on is your timing, try to complete your course in the estimated time provided by the course, or else you have to pay more for extra months which will increase your overall cost of the course.

If you think that the Deep Learning Nanodegree Program is right for you, Udacity is the perfect place for you to take the course and land your dream job.

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