Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient

Hyperparameter Optimization in Machine Learning is a practical, tightly focused guide to making machine learning and deep learning models work better by tuning them more intelligently. Tanay Agrawal keeps the attention on the real problem: how to search, compare, and improve hyperparameters without burning unnecessary time or compute.

From hyperparameters to better model performance

The opening chapters establish the basics clearly, starting with what hyperparameters are, why they matter, and how they influence model behavior. From there, the book moves into the kinds of tuning strategies practitioners actually use, building from simpler approaches toward more advanced optimization methods.

Hands-on techniques across familiar ML tools 💻

The middle of the book covers hyperparameter optimization with scikit-learn and then expands into methods for dealing with time and memory constraints. That makes the material especially useful for readers who want to run more efficient experiments, not just understand the theory behind them.

Bayesian optimization and smarter search

Bayesian optimization receives its own dedicated treatment, along with frameworks such as Hyperopt and Optuna. The book also looks at distributed optimization and the role of automated machine learning, giving readers a broader view of how modern tuning workflows are put together in practice.

Why this ebook stands out

• Clear progression from fundamentals to advanced optimization methods
• Coverage of scikit-learn, Bayesian optimization, Optuna, and AutoML
• Useful emphasis on efficiency, scalability, and model search strategy
• Written for professionals and students working with machine learning

Ideal for ML practitioners

If you work with machine learning or deep learning and want a more structured approach to hyperparameter tuning, this title offers a compact and practical path through the topic. It is a sensible pick for readers who prefer method over guesswork.