Gain Insight into Deep Learning with Udacity's Nanodegree Program

Deep Learning is driving such advances in Artificial Intelligence (AI) that in turn are changing our world. Deep Learning Nanodegree Program welcomes you to build and apply your own deep learning fundamentals along with neural networks to challenges like image classification and generation, time-series prediction, and model deployment. You can become an expert in neural networks and learn to implement them using the deep learning framework PyTorch.

The best part of this Neural network course is that it covers the exciting topic of GANs (which are neural networks that can imitate human actions like text, music, and speech). It can help to restore pictures, generate new faces or characters and much more. It briefly explains the basics of deep learning and touches on the key concepts from machine learning subjects.

In this machine learning nanodegree course, you can collaborate or join via Slack, where you can take the help of your cohorts and have healthy discussions. It is also a good platform for making live open-source contributions in mini Capstones.

It is a well-curated beginner level course spread over 4 months. This online course is taught by experienced faculty members. It usually requires an effort of 10 hours per week just to regulate your course pace. This program has been developed exclusively for students interested in Machine Learning, AI, and Deep Learning and for those who have a basic working knowledge of Python programming. There is no other specific prerequisite for deep learning.

I was taught by Alexis Cook and 5 other instructors. The teaching was entirely project-based; this enabled us to get a lot of hands-on practice. This is probably the best way to study Deep Learning.

The machine learning course content includes a lot of interesting subjects, with (usually) good explanatory videos and walkthroughs. The videos almost always pique interest about the subjects you are about to learn. The bonus for learners is that Udacity has engaged well-known experts to teach some subjects.

The projects and introductory notebooks are another plus point. Here, you will get a lot of hands-on experience writing code. The best thing is you will then have a working code that you can tweak and use for your own projects and a ton of ideas as well to work on afterwards.

Technically, this course has 6 prime modules:

• Introduction to Deep Learning: One can understand the basics of Deep Learning and also the tools that will be used in this course like PyTorch, Python.
• Neural Networks: Here, you can learn about Neural Networks (the backbone of Deep Learning) and how it works.
• Convolutional Neural Networks: Learn how to build convolutional networks and use them to classify images based on patterns and objects that appear in them. Also study how these networks learn data compression and image denoising.
• Recurrent Neural Networks (RNN): Learn how to build your first RNN using PyTorch, and perform sentiment analysis and generate new text from TV scripts.
• Generative Adversarial Networks (GANs): Learn to understand and implement a Deep Convolutional GAN to generate realistic images. Also generate your own first image.
• Deploying a model: Train and deploy your own PyTorch sentiment analysis model, in this module you can learn about AWS, AWS lambda and more.

Currently, I am a Software Development Engineer SDE-Intern at Blackrock. This deep learning best course has certainly boosted my technical clarity and given me confidence to make a career in this field. It helps me while doing data manipulation at my current job.