Chapter 2: Understanding Foundation Models Overview Foundation model design choices (training data, architecture/size, post-training) are increasingly opaque. The training process splits into pre-training (makes model capable) and post-training (aligns model to human preferences). Sampling (how outputs are chosen from all possibilities) is a crucial, often-underestimated factor impacting model behavior and Read more
Chapter 1 Summary: Introduction to Building AI Applications with Foundation Models 1. The Scaling Up of AI Models Foundation models like ChatGPT and Gemini are highly powerful but require enormous compute resources and vast amounts of data. Only a few organizations have the capability to train these models, leading to Read more
1. Fundamentals of Data Engineering This book dives into the world of data engineering, covering everything from how data is collected and stored to how it’s processed and used. It talks about best practices, security, and designing systems that handle data efficiently and safely. 2. Four Thousand Weeks This book Read more
Machine learning can also be defined as the process of solving a practical problem by,1) collecting a dataset, and2) algorithmically training a statistical model based on that dataset Learning can be supervised, semi-supervised, unsupervised, and reinforcement. Supervised Learning In supervised learning the data analyst works with collection of labelled examples Read more
Every once in a while, we stumble upon a book that profoundly impacts our lives, instigates change, and even redefines our perspectives. Today, I bring you a curated list of 12 transformative books that encompass personal development, professional growth, financial wisdom, entrepreneurial journeys, software engineering, spirituality, mental health, and leadership Read more