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Deep Learning Literacy - Practical Application

11 courses
14 hours

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

This path is focused on Deep Learning in action. We have pulled a series of examples to demonstrate how deep learning is embedded in our day to day lives. These are just in time sort of courses that reflect the journey from problem to solution.

The path is curated for Data enthusiasts that are eager to learn about Deep learning and foray into Data centered roles like Data scientist. Though this path will contain workable solutions, there is no requirement for the learner to have any background into Machine Learning or Deep learning. Intention is to have sandboxes for the path.

Courses in this path

Beginner

This path is designed to explore the application of Deep Learning in our day to day lives. This section focuses on a few industry examples of Deep Learning Practical applications.

Deep Learning Application for Healthcare

by Colin Matthews
1h 3m 6s
4.5 (18)

Deep Learning Application for Marketing

by Netta Tzin
53m 6s

Deep Learning Application for Retail

by Trent McMillan
1h 35s
5.0 (10)

Intermediate

This section of the path focuses on RNN, CNN and GAN’s implementation and exploring how deep learning solves problems like automating image captioning and sentiment classification.

Implement Image Recognition with a Convolutional Neural Network

by Pratheerth Padman
1h 30m 58s

Implement Text Auto Completion with LSTM

by Biswanath Halder
1h 34m 23s

Sentiment Classification with Recurrent Neural Networks

by Biswanath Halder
1h 32m 20s

Advanced

This section explores practical examples of word embedding, Sentence classification, Named Entity Recognition and Recommendation Engines.

Implement Natural Language Processing for Word Embedding

by Axel Sirota
1h 33m 53s
3.0 (11)

Implement Named Entity Recognition with BERT

by
39m 8s

Build a Rating Recommendation Engine with Collaborative Filtering

by Pratheerth Padman
1h 20m 42s
4.7 (15)

Build an Object Detection Model with Python

by
1h 12m 41s

Build a Model for Anomaly Detection in Time Series Data

by Pratheerth Padman
1h 13m 45s
4.8 (16)

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Implement Image recognition with Convolutional Neural Network Implement Text Auto Completion with LSTM Implement Image Captioning with Recurrent Neural Networks Implement Natural Language Processing for Word Embedding Implement Sentence Classification with BERT Implement Named Entity Recognition with BERT Sentiment Classification with Recurrent Neural Networks Build a Rating Recommendation Engine with Collaborative Filtering Build an Object Detection Model with Python Build a model for Anomaly Detection in Time Series Data Implement Text to Image Translation with Generative adversarial Networks Deep Learning Application for Healthcare Deep Learning Application for Marketing Deep Learning Application for Finance Deep Learning Application for Retail

Experience

Understanding of algorithms used in the path. Though it is desired but not mandatory Understanding of Deep Learning Key concepts

Learn with the best

Colin Matthews

Colin is an experienced technical professional specializing in healthcare technology. Whether it's building full-stack web apps for side-projects, creating content online, or managing developers in his day job as a Product Manager, Colin always shows up with a smile and a willingness to learn.

Netta Tzin

Netta is a Product and Growth expert, who’s been utilizing her talents as a Product Manager in insure-tech and music-tech fields, as well as a Product consultant for various organizations. Her background combines both academic degrees in Industrial Engineering (Bsc.) and in International Management (Msc.), along with work experience of over 10 years in strategizing, structuring, and executing projects and products, both in startups and corporations. In the past few years, Netta has focused on le... more

Trent McMillan

Trent has a master's degree in Information Systems from Brigham Young University and a love for all things data. When he isn't on his computer he enjoys dirt bikes, DIY projects, and spending time with family and friends.

Pratheerth Padman

Pratheerth is a Data Scientist who has entered the field after an eclectic mix of educational and work experiences. He has a Bachelor's in Engineering in Mechatronics from India, Masters in Engineering Management from Australia and then a couple of years of work experience as a Production Engineer in the Middle East. Then when the A.I bug bit him, he dropped everything to dedicate his life to the field. He is currently working on mentoring, course creation and freelancing as a Data Scientist.

Biswanath Halder

Biswanath is a Data Scientist who has around nine years of working experience in companies like Oracle, Microsoft, and Adobe. He has extensive knowledge of Machine Learning, Deep Learning, and Reinforcement Learning. He specializes in applying Machine Learning and Deep Learning techniques in complex business applications related to computer vision and natural language processing. He is also a freelance educator and teaches Statistics, Mathematics, and Machine Learning. He holds a Master's degre... more

Axel Sirota

Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.