Deep Learning (Cont. II)

Introduction to Deep Lenning (cont.) Part II

Introduction to Deep Learning Part II: Applications

This section covers the growing importance of the use of Deep Learning in Big Data Applications and Analytics. The Intro Unit is an introduction to the technology with examples incidental. The MNIST Unit covers an example on Google Colaboratory. The Technology Unit covers deep learning approaches in more detail than the Intro Unit. The Tech Unit covers the deep learning technology in more detail. The Application Unit cover deep learning applications at different levels of sophistication.

Applications of Deep Learning Unit Summary This unit is an introduction to deep learning with currently 7 lessons

Recommender: Overview of Recommender Systems

Recommender engines used to be dominated by collaborative filtering using matrix factorization and k’th nearest neighbor approaches. Large systems like YouTube and Netflix now use deep learning. We look at sysyems like Spotify that use multiple sources of information.


Retail: Overview of AI in Retail Sector (e-commerce)

The retail sector can use AI in Personalization, Search and Chatbots. They must adopt AI to survive. We also discuss how to be a seller on Amazon


RideHailing: Overview of AI in Ride Hailing Industry (Uber, Lyft, Didi)

The Ride Hailing industry will grow as it becomes main mobility method for many customers. Their technology investment includes deep learning for matching drivers and passengers. There is huge overlap with larger area of AI in transportation.


SelfDriving: Overview of AI in Self (AI-Assisted) Driving cars

Automobile Industry needs to remake itself as mobility companies. Basic automotive industry flat to down but AI can improve productivity. Lesson also discusses electric vehicles and drones


Imaging: Overview of Scene Understanding

Imaging is area where convolutional neural nets and deep learning has made amazing progress. all aspects of imaging are now dominated by deep learning. We discuss the impact of Image Net in detail


MainlyMedicine: Overview of AI in Health and Telecommunication

Telecommunication Industry has little traditional growth to look forward to. It can use AI in its operation and exploit trove of Big Data it possesses. Medicine has many breakthrough opportunities but progress hard – partly due to data privacy restrictions. Traditional Bioinformatics areas progress but slowly; pathology is based on imagery and making much better progress with deep learning


BankingFinance: Overview of Banking and Finance

This FinTech sector has huge investments (larger than other applications we studied)and we can expect all aspects of Banking and Finance to be remade with online digital Banking as a Service. It is doubtful that traditional banks will thrive


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