Here’s a glimpse into my collection of books I’ve gathered over the last couple of years. Each volume has been a source of enlightenment, broadening my understanding across various subjects and contexts. For comprehensive reviews, please refer to View all my reviews.
Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection by Tarek A. Atwan
My rating: 5 of 5 stars
The thing that I like about this book is how the author provides hands-on examples when laying out every concept. Traditional time-series methodology to modern ones have been clearly explained alongside best practices for improving forecasting evaluation metrics that affect business-decision making process. The other new knowledge that I really like diving deeper into this book is how the time-series split methodology like direct, multi output, and recursive strategy that play vital role in improving evaluation metrics have been laid out clearly. I highly recommend this book for anyone interested in upskilling their forecasting techniques.
Build an AI Agent by Jungjun Hur
This book delivers what the title offers. Building ai agent from scratch really shapes my understanding regarding how ai agent works and some critical elements that need careful consideration before deploying agents such as managing structured json output, memory management, human in the loop review, and evaluation. This book clarifies all of these things alongside hands-on exercise.
Data Analysis for Business, Economics, and Policy by Gábor Békés
My rating: 5 of 5 stars
a comprehensive book that explains data analysis from A-Z in an easy way. What really stands out from this book is how the author breaks down every concept along with hands-on approaches(nice visualization) that makes me really enjoy reading chapter by chapter. I would highly recommend this book for making data analysis more effective and structured.
Domain-Specific Small Language Models: Efficient AI for local deployment by Guglielmo Iozzia
My rating: 5 of 5 stars
great read in understanding small language model for limited computing power and resources.
Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models by Jim Frost
My rating: 5 of 5 stars
best linear regression book that I have ever read. extremely concise book alongside practical lab in plain language. really recommend this amazing book especially the narrative that really helps me clearly comprehend all chapters.
Writing for Developers: Blogs that get read by Piotr Sarna
My rating: 5 of 5 stars
interesting book to help me write better as developer. This book covers personal perspective to AI-based one in terms of writing better blogpost. This book is gonna be my go-to reference for prioritizing essential points needed in my future blogpost.
Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking Discoveries by Jim Frost
My rating: 5 of 5 stars
The way the author explains the statistics in a comprehensive and hands-on way helps me understand the STATISTICS WAY to solve real world problems for non-technical people who do not have statistical knowledge in a really good fashion. I salute to the author for explaining such theories. hidden gems book.
Geospatial Data Science Essentials: 101 Practical Python Tips and Tricks by Milán Janosov
My rating: 5 of 5 stars
This book outlines the geospatial essentials based on what the title wanna offer. clear and concise. Going from the very basic of geospatial data and the attributes like vector and raster data along the way to interacting with various type of them. Great learning experience from this book.
Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models by Aldo Marzullo
My rating: 5 of 5 stars
really enjoy reading this book. It really dives deeper into how graph machine learning can help capture pattern and relationship in our data and do various prediction-based approach to help make informed decision-making. i enjoy the use cases chapters in this book that focus on hands-on approach to utilize graph ML on fraud detection problem(tabular data) namely processing the data to form graph structure, analyze the structure to make prediction and some advices regarding the results.
Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications by Joseph Babcock
My rating: 5 of 5 stars
interesting book to read with simple explanation and hands-on experience working with the data. I really love the LLMs content especially the transformer architecture optimization part that makes either fine-tuning or training become much more efficient.
Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models by Luca Antiga
My go-to resource to reviewing Pytorch from basic to advanced. The author really explains the concept clearly. The visualization along with the hands-on instances helps me get better idea of how Pytorch works under the hood. Note : I also read the first edition of this book and i found the 2nd edition more enlightening with new content tailored to current advances in artificial intelligence.
System Design on AWS: Building and Scaling Enterprise Solutions by Jayanth Kumar
My rating: 5 of 5 stars
detailed and easy-to-understand way to system design on AWS. One thing i really like about this book is from compute, storage and networking services within AWS, the author makes everyday instances in laying out system design on AWS.
Build: An Unorthodox Guide to Making Things Worth Making by Tony Fadell
My rating: 5 of 5 stars
I am impressed by how Tony Fadell tells his journey from working in big tech to even building his company along the way to where he is right now. I am not like reading a book through this book. I am directly like following his story to see ups and downs of his journey and I also see some fascinating story when google acquired his company Nest where he kept emphasizing the culture of the company about the culture of big tech where offices with free snacks and lots of food and he changed it to something extraordinary on behalf of nes employee family and next generation of NES employees. What a story! Thank you for writing this beautiful story. I learn a lot in this book.
Think Stats: Exploratory Data Analysis by Allen Downey
My rating: 5 of 5 stars
This book explains how EDA should be approached from statistical perspective. I really enjoy reading this book and it helps me dive deeper into analyzing data better. What really stands out is its hands-on examples that effectively demystify every concept.
Causal Inference in Python: Applying Causal Inference in the Tech Industry by Matheus Facure
My rating: 5 of 5 stars
reading the first chapter alongside simple instances of causal inference use cases genuinely helps me comprehend it in an intuitive way. The author really knows how to explain it better. It’s going to be my go-to resource when it comes to explaining the impact of predictions I would like to present for my clients. i could say this is the book you need to comprehend causal inference in a industry-based perspective. Kudos to the author!
Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data by Jonas Christensen
My rating: 5 of 5 stars
I loved this book so much. I wish I had it when I started over my journey diving into ML. So practical and well written.
Hands-On Generative AI with Transformers and Diffusion Models by Omar Sanseviero
My rating: 5 of 5 stars
This book offers a different level of explanation when it comes to demystifying complex topic. I like how the author breaks down every complex content in a structural way yet easy to follow. Reading technical theory titled Building Large Language model from scratch by Sebastian followed by this book will be a good combination to get the natural feeling of comprehending every narratives the author brings in this book. I really enjoy reading every page of it and It is gonna be my go-to reference for my for future project.