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Free Download Machine Learning Infrastructure and Best Practices for Software Engineers: Take your machine learning software from a prototype to a fully fledged software system by Miroslaw Staron
English | January 31, 2024 | ISBN: 1837634068 | 346 pages | PDF | 24 Mb
Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products

Free Download Machine Learning For Quant Strategies by Surendra Singh
English | June 26, 2024 | ISBN: N/A | ASIN: B0D899GHJ6 | 234 pages | EPUB | 0.35 Mb
Unlock the full potential of algorithmic trading with "Algorithmic Trading Mastery: Advanced Quant Strategies with Machine Learning, Time Series Analysis, and Bayesian Statistics." This comprehensive guide is designed for finance professionals, traders, and developers looking to elevate their trading strategies using advanced quantitative methods and state-of-the-art technologies.What You'll LearnAlgorithmic Trading Foundations: Understand the fundamentals of algorithmic trading, its history, key concepts, and the benefits and challenges it presents.Data Preparation and Analysis: Master the importance of high-quality data, data cleaning, preprocessing, and feature engineering to build robust trading models.Machine Learning in Finance: Explore supervised, unsupervised, and reinforcement learning techniques and their application in financial markets.Advanced Trading Strategies: Delve into momentum strategies, mean reversion, statistical arbitrage, pairs trading, and Kalman filter-based strategies.Risk Management and Portfolio Optimization: Learn to manage risk using Value at Risk (VaR), Conditional VaR (CVaR), and optimize portfolios with advanced techniques.Deep Learning and AI: Utilize deep learning models, LSTM networks, GANs, and reinforcement learning for developing sophisticated trading algorithms.Real-Time Trading Systems: Implement real-time trading systems with a focus on software and hardware requirements, execution algorithms, and real-time risk management.Ethics and Compliance: Ensure ethical practices and regulatory compliance in algorithmic trading, and learn from case studies.Future Directions: Stay ahead with insights into machine learning and AI trends, quantum computing, and decentralized finance (DeFi).Key Features500+ Pages of In-Depth Content: Covering the latest in machine learning-based systematic trading techniques.Practical Examples and Code Snippets: Implement advanced quant methods using easy-to-read R and Python code.Comprehensive Appendices: Additional resources, Python and R code snippets, and a detailed glossary for quick reference.Expert Insights: Authored by Surendra Singh, a finance professional with over two decades of experience in financial technology.

Free Download Machine Learning For Finance: A Workshop Approach by Surendra Singh, A.J. Codefield
English | June 20, 2024 | ISBN: N/A | ASIN: B0CZ7B3B5P | 100 pages | EPUB | 0.33 Mb
Unlock the power of machine learning in the finance industry with Machine Learning for Finance: A Workshop Approach. This comprehensive guide offers a hands-on approach to mastering machine learning techniques and applying them to real-world financial scenarios.

Free Download Machine Learning Evaluation: Towards Reliable and Responsible AI
English | 2025 | ISBN: 1316518868 | 427 Pages | PDF | 5 MB
As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.

Dl4All - Download free For All: software and educational resource.

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