Feature Engineering: Fundamentals and Best PracticesA summary of Chapter 5 of the Book “Designing Machine Learning Systems” by Chip HuyenApr 12Apr 12
Analysis of chapter 0 of the book “The Principles of Deep Learning Theory”This perspective leads us to consider that neural networks are governed by nearly-gaussian distributions.Apr 12Apr 12
10 key points of Chapter 4 of Chip Huyen’s book, “Designing Machine Learning Systems”Training data serves as the cornerstone of machine learning algorithms, dictating their effectiveness and reliability. Chapter 4 of the…Apr 5Apr 5
“On the Societal Impact of Open Foundation Models” —My Personal ReviewThe favorable aspects and the drawbacks of model sharingApr 5Apr 5
Designing Machine Learning Systems: A Brief Summary of Chapters 1–3The development of machine learning systems is a multifaceted process that encompasses far more than just algorithmic considerations. The…Mar 21Mar 21
Summary of chapter 3: Data Engineering FundamentalsChapter 3 covers the fundamentals of data engineering and provides a detailed look at the different steps and considerations involved in…Mar 21Mar 21
Summary of chapter 2: Introduction to Machine Learning Systems DesignChapter 2 introduces the design of machine learning systems by adopting a systematic approach to MLOps. It highlights the importance of…Mar 21Mar 21
Summary of chapter 1: Overview Of Machine Learning SystemsChapter 1, “Overview Of Machine Learning Systems” offers a comprehensive overview of machine learning systems and their rise from deep…Mar 21Mar 21
Transfer Learning on NLP using TensorFlowUsing the models from TensorFlowHub, Fine Tuning them, comparing these models with a model trained from scratch and creating a hybrid modelDec 17, 2020Dec 17, 2020
Using the AlexNet on a Cat-Dog classificationWith Hyperparameter tuning and Data AugmentationNov 30, 2020Nov 30, 2020
