JF Solutions Launches Four-Volume Deep Learning Theory Series to Bridge the Gap Between Implementation and Practical Application

## Background and Challenges

Many engineers can run tutorials and use libraries, but struggle to articulate why a specific architecture is chosen or what causes training failures. There has long been a gap between superficial implementation examples and overly academic theory books. This series was designed to fill that void by fostering the decision-making skills required in professional practice.

## Overview

The fundamental wall faced by engineers—the inability to make informed decisions despite being able to build models—often stems from a lack of systematic organization regarding theory and architectural selection criteria. This series provides a framework for understanding 'why' things work for beginners and the applied expertise for experienced engineers to explain 'why' they choose specific models. It is not a mere collection of code examples or an abstract theory text; it is engineered specifically to develop professional decision-making capabilities.

Totaling 2,215 pages across four volumes, the series covers a vast amount of information required to systematically learn modern deep learning, from mathematical foundations to generative models, deep reinforcement learning, and MLOps.

- Vol. 1: Environment and Theoretical Foundations of DL
- Vol. 2: Basics of Deep Learning, CNN, RNN, and Transformer
- Vol. 3: DL Applications (1) (Image, Object Detection, NLP, Audio)
- Vol. 4: DL Applications (2) (Generative Models, Reinforcement Learning, XAI, MLOps)

Derivations of mathematical formulas are paired with explanations of their practical necessity, ensuring understanding rather than memorization, enabling engineers to evaluate new technologies independently.

## Intended Audience

This series is suitable for engineers who can implement models but lack a decision-making framework for model selection, those who are unsure about applying research papers to their projects, and trainers seeking materials that connect theory with practical implementation.

- Future plans include volumes on practical implementation (MLOps), Python fundamentals, AI Agents, and RAG.

## About the Author

Author Hitoshi Furoi is a business architect and DX/PMO consultant holding multiple national qualifications, including IT Strategist and System Auditor. Based on over 20 years of experience, Furoi wrote this series under the belief that the difference between engineers who just know theory and those who can select architectures dictates the success of AI projects.

## Key Features

- Pairs mathematical derivation with practical significance, maintaining context for every mechanism and calculation.
- Includes two types of indexes at the end of each volume: a problem-solving index and a technical terminology index, designed for professional reference.
- Large-format design (B5 size, over 460 pages per volume) intended as a reference book to be kept on hand.

*Currently only available in digital format; print versions are planned for the future.