# Article: Upcoming Live Session on Deep Learning Algorithms and Recommender Systems Design
## Introduction
We are thrilled to announce our next live session, exclusively for enrolled course participants. This special event is scheduled for Sunday, December 12th, from 7 PM to 9:30 PM. The focus will be on deep learning algorithms, architectures, and system design, particularly as they pertain to building recommender systems using advanced deep learning techniques.
## Session Overview
The live session will delve into the intricacies of designing real-world recommender systems. While collaborative filtering and single deep learning architectures form foundational knowledge, challenges such as cold start and the integration of recency and frequency metrics necessitate a more nuanced approach. We will explore various design choices and implementation strategies through high-level system design discussions.
## Case Studies and Industry Examples
Drawing from real-world case studies, we will examine the approaches taken by leading companies in the field:
- **YouTube**: Insights into their recommendation algorithms and system design.
- **Alibaba and Amazon**: Strategies employed by these e-commerce giants for personalized recommendations.
- **NVIDIA and LinkedIn**: Research findings and practical applications from these tech leaders.
Additionally, we will consider innovative solutions from startups, particularly those leveraging limited datasets to develop novel algorithms and architectures. Startups often face unique challenges but can offer fresh perspectives on tackling complex problems with resource constraints.
## Prerequisites for Participation
To fully engage with the content, participants should have a solid grasp of:
- **Deep Learning Fundamentals**: Including multi-layered perceptrons, transformers, CNNs, and LSTMs.
- **Matrix Factorization Techniques**: Essential for understanding traditional recommender systems before moving to advanced deep learning approaches.
## Session Format
The session will be conducted via the LiveMe platform. The format is highly interactive, encouraging participants to engage through questions and collaborative problem-solving. This approach mirrors our previous successful live sessions, fostering a dynamic and enriching discussion environment.
## Conclusion
We look forward to an exciting and interactive session where we can explore cutting-edge deep learning applications in recommender systems. Whether you're tackling system design challenges or seeking innovative solutions for limited data scenarios, this session promises valuable insights and discussions. Join us on Sunday, December 12th, at 7 PM for what promises to be a thought-provoking and engaging experience.
See you all soon!
"WEBVTTKind: captionsLanguage: enhi friends our next live session for all of our course enrolled students will be on the coming sunday which is the 12th of december and this live session is a two hour live session roughly two to two and a half hours from 7 pm to 9 to 9 30 p.m and in this live session we will specifically focus on deep learning algorithms architectures and some system design corresponding to how we design recommender systems using deep learning algorithms in this in this live session itself in addition to discussing various architectures that are possible we will spend a good amount of time on the machine learning system design because when we design a real-world recommender systems it's not just about a collaborative filtering approach or one deep learning architecture you will get into problems of cold start you want to do recommender systems taking recency and frequency into consideration so we'll discuss about various design choices that exist and how you can actually implement this through some high level system design discussions again as usual we will make this whole case based where we will take real world case studies both of the architecture and the system design that companies have published either in their research blogs or in their engineering blogs so we will take examples from companies like youtube a very popular set of algorithms and system design that youtube specifically designed for their recommended systems we'll also take up examples from companies like alibaba and amazon we'll take some research work that is published by nvidia and linkedin in this mix we will also throw in a few startups based on their engineering blogs or research papers because startups typically may not have as large a data set that somebody like alibaba or youtube might have so what innovative algorithms architectures and design choices can startups make especially for this sort of problem that they may not have lot of data to start with cool so the prerequisites for this live session would be a good understanding of deep learning algorithms starting from simple multi-layered perceptrons to transformers cnns lstms etc and also a good understanding of matrix factorization based strategies for recommender systems because once you know these concepts it's very easy for us to build on top of these concepts introduce new architectures and new algorithms and also some design choices that you have when you have to deploy these systems again this whole live session will be conducted via live me via her airmate platform and this will be between 7 pm and 9 pm on the coming sunday so see you i'm looking forward to a very exciting discussion with you just like our previous live sessions it will be very interactive i'll keep posing questions let us try to solve this problem ourselves as we are trying to also understand other solutions in a very interactive question based and discussion based format so looking forward to seeing all of youhi friends our next live session for all of our course enrolled students will be on the coming sunday which is the 12th of december and this live session is a two hour live session roughly two to two and a half hours from 7 pm to 9 to 9 30 p.m and in this live session we will specifically focus on deep learning algorithms architectures and some system design corresponding to how we design recommender systems using deep learning algorithms in this in this live session itself in addition to discussing various architectures that are possible we will spend a good amount of time on the machine learning system design because when we design a real-world recommender systems it's not just about a collaborative filtering approach or one deep learning architecture you will get into problems of cold start you want to do recommender systems taking recency and frequency into consideration so we'll discuss about various design choices that exist and how you can actually implement this through some high level system design discussions again as usual we will make this whole case based where we will take real world case studies both of the architecture and the system design that companies have published either in their research blogs or in their engineering blogs so we will take examples from companies like youtube a very popular set of algorithms and system design that youtube specifically designed for their recommended systems we'll also take up examples from companies like alibaba and amazon we'll take some research work that is published by nvidia and linkedin in this mix we will also throw in a few startups based on their engineering blogs or research papers because startups typically may not have as large a data set that somebody like alibaba or youtube might have so what innovative algorithms architectures and design choices can startups make especially for this sort of problem that they may not have lot of data to start with cool so the prerequisites for this live session would be a good understanding of deep learning algorithms starting from simple multi-layered perceptrons to transformers cnns lstms etc and also a good understanding of matrix factorization based strategies for recommender systems because once you know these concepts it's very easy for us to build on top of these concepts introduce new architectures and new algorithms and also some design choices that you have when you have to deploy these systems again this whole live session will be conducted via live me via her airmate platform and this will be between 7 pm and 9 pm on the coming sunday so see you i'm looking forward to a very exciting discussion with you just like our previous live sessions it will be very interactive i'll keep posing questions let us try to solve this problem ourselves as we are trying to also understand other solutions in a very interactive question based and discussion based format so looking forward to seeing all of you\n"
