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{ "item_title" : "Core Machine Learning and Deep Learning", "item_author" : [" Axionics Ltd "], "item_description" : "This all-in-one resource walks you through the full spectrum of machine learning. From foundational math to supervised, unsupervised, and reinforcement learning, it provides a thorough understanding of core ML and DL principles. Ideal for both beginners and advancing practitioners, it includes hands-on tools, practical algorithms, and insights into neural networks and optimization techniques. What's Inside?✔ Regression, classification, and ensemble models✔ Clustering, anomaly detection, and dimensionality reduction✔ Reinforcement learning: Q-learning, DQNs, and MDPs✔ Neural networks: MLPs, backpropagation, and activation functions✔ Optimization: SGD, Adam, and regularization✔ Transfer learning, feature extraction, and deployment✔ Bias-variance tradeoff and model evaluation Why This Book?Because understanding the core mechanics of learning systems is key to building reliable, ethical, and high-performing AI models.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/79/829/119/9798291198339_b.jpg", "price_data" : { "retail_price" : "23.99", "online_price" : "23.99", "our_price" : "23.99", "club_price" : "23.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Core Machine Learning and Deep Learning|Axionics Ltd

Core Machine Learning and Deep Learning : Supervised, Unsupervised, Reinforcement Learning, Neural Networks, Optimization, and Transfer Learning

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Overview

This all-in-one resource walks you through the full spectrum of machine learning. From foundational math to supervised, unsupervised, and reinforcement learning, it provides a thorough understanding of core ML and DL principles. Ideal for both beginners and advancing practitioners, it includes hands-on tools, practical algorithms, and insights into neural networks and optimization techniques. What's Inside?
✔ Regression, classification, and ensemble models
✔ Clustering, anomaly detection, and dimensionality reduction
✔ Reinforcement learning: Q-learning, DQNs, and MDPs
✔ Neural networks: MLPs, backpropagation, and activation functions
✔ Optimization: SGD, Adam, and regularization
✔ Transfer learning, feature extraction, and deployment
✔ Bias-variance tradeoff and model evaluation Why This Book?
Because understanding the core mechanics of learning systems is key to building reliable, ethical, and high-performing AI models.

This item is Non-Returnable

Details

  • ISBN-13: 9798291198339
  • ISBN-10: 9798291198339
  • Publisher: Independently Published
  • Publish Date: July 2025
  • Dimensions: 9 x 6 x 0.36 inches
  • Shipping Weight: 0.51 pounds
  • Page Count: 168

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