A Deep Dive into Loss Functions for Optimized Predictions
A Deep Dive into Loss Functions for Optimized Predictions
13 Sep 202413:09pm - 13 Sep 202414:09pm
A Deep Dive into Loss Functions for Optimized Predictions
About the Event
Explore the critical role of loss functions in optimizing machine learning models during our upcoming DataHour session. We'll delve into how selecting the appropriate loss function can significantly impact model accuracy, reliability, and overall performance. This session will provide a comprehensive overview of loss functions, including fundamental concepts and their significance in supervised learning, mathematical foundations of various loss functions, optimization techniques with a focus on gradient descent, comparative analysis of loss functions for regression and classification tasks, and strategies for mitigating overfitting.
We'll examine a case study from CommerceIQ, where implementing a Zero Inflated Tweedie loss function led to substantial reductions in prediction errors. This real-world application demonstrates the tangible benefits of tailoring loss functions to specific problem domains.
Key topics covered include hyperparameter tuning methodologies, criteria for selecting optimal loss functions based on data characteristics and project objectives, techniques for developing custom loss functions (e.g., variants of mean squared error), and best practices for implementation and integration within existing model architectures.
By the end of the session, participants will have gained practical insights into leveraging loss functions to enhance model performance across diverse machine learning applications. Join us to refine your approach to predictive modeling and elevate the efficacy of your algorithms.
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
Who is this DataHour for?
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
About the Speaker
Anuj Sao is a Senior Lead Data Scientist at CommerceIQ, where he plays a pivotal role in developing and mentoring AI projects in the Retail Media Management space. With over 11 years of experience in data science, Anuj has honed his expertise in machine learning, deep learning, and Ad Tech-related problem-solving.
At CommerceIQ, Anuj has spearheaded the development of the Incrementality Product and built a Bid and Budget Optimizer for Ecommerce. He has also worked on Bid Landscape Forecasting for Video Ads in Games for DSP, amongst other works.
Anuj holds a Btech + Mtech Dual Degree in Industrial Engineering and Management from IIT Kharagpur, graduating in 2013. You can reach him on LinkedIn.
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