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AI models can become so complex that even experts have difficulty understanding themand forget the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. You'll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when you're facing a problem that demands the predictive power of ahard-to-interpret deep learning model.
AI models can become so complex that even experts have difficulty understanding themand forget the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. You'll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when you're facing a problem that demands the predictive power of ahard-to-interpret deep learning model.
Ajay Thampi is a machine learning engineer at a large tech company primarily focused on responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781617297649 |
| ISBN-10: | 161729764X |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Thampi, Ajay |
| Hersteller: | Manning Publications |
| Maße: | 231 x 188 x 19 mm |
| Von/Mit: | Ajay Thampi |
| Erscheinungsdatum: | 17.10.2022 |
| Gewicht: | 0,608 kg |
Ajay Thampi is a machine learning engineer at a large tech company primarily focused on responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781617297649 |
| ISBN-10: | 161729764X |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Thampi, Ajay |
| Hersteller: | Manning Publications |
| Maße: | 231 x 188 x 19 mm |
| Von/Mit: | Ajay Thampi |
| Erscheinungsdatum: | 17.10.2022 |
| Gewicht: | 0,608 kg |
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