Learning Tips for Becoming an Expert with Udacity's Deep Learning Nanodegree Program

Deep learning is driving such advances in Artificial Intelligence (AI) that are changing our world. Deep Learning Nanodegree Program welcomes you to build and apply your own deep 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 course teaches you to build complex convolutional networks for image recognition, recurrent networks for sequence generation, Generative Adversarial Networks (GANs) for image generation, and learn how to deploy models accessible from a website. The best part of this course is that it covers the exciting topic of GANs (which are neural networks that can imitate human actions like text, music, speech).

It can help to restore pictures, generate new faces or characters and much more. With this course, you will develop a crucial understanding of Neural Network Architecture, GANs, Deep Learning, Natural Language Processing (NLP), etc. In this course, you can collaborate or join via Slack. Here you can take the help of your cohorts and have healthy discussions. It is also a good platform for 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. 

This Nanodegree program accepts all applicants regardless of experience and specific educational background. The modules of this course are detailed, comprehensive, and practical. Industries, tech firms, startups and government agencies are all areas where one can work as a deep learning engineer. This program is developed to help learners build deep learning models that revolutionize AI. 

The assessment is based on learners completing assignments and undertaking projects under various modules throughout the degree. These can be research-based or concept specific. The final assessment is made on the basis of the learner’s performance in the Capstone project. 

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: 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 working in Net Solutions as a Python Developer in one of its scraping projects. This course has certainly boosted my technical clarity and confidence, thus elevating my career prospects. If you are willing to learn more about Deep Learning, then I would suggest the course on Deep Reinforcement Learning.