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ML Optimization Techniques Boosting Model Performance: Fine-tune ML models for accuracy and computational efficiency

by Isandro Myles
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₹1,654.00
Original price ₹1,654.00
Original price ₹1,654.00
₹1,654.00
Current price ₹1,654.00

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Book cover type: Paperback
  • ISBN13: 9798265144638
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 174
  • Original Price: USD 16.99
  • Language: English
  • Edition: N/A
  • Item Weight: 241 grams
  • BISAC Subject(s): Software Development & Engineering / General

Speed and accuracy aren't enemies-they're a design choice. ML Optimization Techniques: Boosting Model Performance is your practical playbook for squeezing more signal from the same data, cutting training time, and shipping models that perform where it counts: in production.

Inside, you'll move beyond "try a bigger model" to a systematic approach that stacks small, compounding wins-better data splits, smarter tuning, tighter regularization, faster math, and leaner deployments. You'll learn what to measure, which knobs matter most, and how to make trade-offs you can defend to stakeholders.

What you'll be able to do:

  • Choose and tune optimizers (SGD, AdamW, LBFGS) with schedules that actually converge

  • Run Bayesian, random, and evolutionary searches that beat grid search with fewer trials

  • Avoid leakage with robust CV schemes and fail-fast early stopping

  • Boost generalization with regularization, augmentation, and calibrated probability outputs

  • Fix class imbalance (weights, focal loss, stratified sampling) without skewing metrics

  • Stack and blend models the right way (OOF folds, diversity, calibration)

  • Compress models (pruning, quantization, distillation) for edge and low-latency serving

  • Accelerate training with mixed precision, vectorization, caching, and efficient data loaders

  • Profile bottlenecks (CPU/GPU, I/O) and pick wins that matter for wall-clock time

  • Operate with MLOps discipline: champion/challenger, drift monitoring, rollback plans

Written for data scientists, ML engineers, and practitioners who care about results, this book favors clarity over hype and repeatable techniques over lucky runs.

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