Check out Ajay Thampi's book 📖 Interpretable AI | [ Ссылка ] 📖 DISCOUNT CODE ⭐ watchthampi40 ⭐ This live coding session by Ajay Thampi teaches how to interpret complex machine learning models using Python's interpretability techniques.
A word from the author:
"With breakthroughs in areas such as image recognition, natural language understanding, and board games, AI and machine learning are revolutionizing various industries such as healthcare, manufacturing, retail, and finance. As complex machine learning models are being deployed into production, the understanding of them is becoming very important. The lack of a deep understanding can result in models propagating bias and we’ve seen examples of this in criminal justice, politics, retail, facial recognition, and language understanding. All of this has a detrimental effect on trust and from my experience, this is one of the main reasons why companies are resisting the deployment of AI across the enterprise. Explaining or interpreting AI is a hot topic in research and the industry, as modern machine learning algorithms are black boxes and nobody really understands how they work. Moreover, there is EU regulation now to explain AI under the GDPR “right to explanation”. Interpretable AI is therefore a very important topic for AI practitioners."
Watch the full video at: [ Ссылка ]
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Interpretable AI | [ Ссылка ]
To save 40% off this book use discount code: watchthampi40
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About the author:
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.
About the book:
Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting-edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and open source libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you to identify the patterns your model has learned and presents best practices for building fair and unbiased models. When you’re done, you’ll be able to improve your AI’s performance during training and build robust systems that counteract errors from bias, data leakage, and concept drift.
