Books Authored

Description:
This comprehensive guide delves into neural networks and deep learning techniques using TensorFlow and Keras. It covers a range of topics, including regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs). The book emphasizes practical applications, providing clear explanations and extensive code samples to help readers build and deploy supervised, unsupervised, deep, and reinforcement learning models.

Key Features:

• Comprehensive coverage of deep learning models and techniques.
• Practical examples with Python code implementations.
• Focus on real-world applications across various domains.

Reviews:
Readers have praised the book for its practical approach and comprehensive coverage. One reviewer noted, “The book focuses admirably well on the practical side of many variants of neural networks… Readers also get to see actual Python code implementing each of the NN variants.”

You can purchase book from Amazon , Kindle edition is also available.

Description:
This book explores the integration of artificial intelligence (AI) and the Internet of Things (IoT), providing expert techniques for developing smarter IoT systems. It covers various aspects of AI implementation to enhance IoT solutions, including machine learning, deep learning, genetic algorithms, and reinforcement learning. The book also discusses data gathering, preprocessing, and distributed processing for IoT data.

Key Features:

• Application of AI techniques to IoT data.
• Coverage of both supervised and unsupervised machine learning methods.
• Real-world case studies in personal IoT, industrial IoT, and smart cities.

Reviews:
Readers appreciate the book’s comprehensive approach to AI and IoT integration. One reviewer mentioned, “The book covers a wide spectrum of AI topics from machine learning to deep learning, genetic algorithms, reinforcement learning, H2O, etc., to build smart AI models.”

Here is the Amazon link to purchase.

Description:
This book focuses on designing and building resilient, private, fair, and transparent machine learning models. It addresses the challenges of responsible AI development, providing insights into creating platforms and models that adhere to ethical standards and promote trustworthiness in AI applications.

Key Features:

• Guidance on implementing ethical principles in AI model design.
• Strategies for ensuring privacy, fairness, and transparency in AI systems.
• Discussion of real-world applications and case studies.

Amazon Link

Description:
Co-authored with Ankit Jain and Armando Fandango, this book guides readers through 13 real-world projects utilizing TensorFlow’s capabilities. Projects range from detecting exoplanets and sentiment analysis to playing Pac-Man using deep reinforcement learning. The book emphasizes practical applications, providing hands-on experience with TensorFlow’s ecosystem.

Key Features:

• Implementation of diverse machine learning projects using TensorFlow.
• Exploration of TensorFlow’s modules, including TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite.
• Focus on real-world applications across various domains.

Amazon link to purchase.

Description:
Co-authored with Antonio Gulli, this cookbook offers over 90 unique recipes to solve artificial intelligence-driven problems using Python and TensorFlow 1.x. It covers various neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book provides hands-on solutions for implementing deep learning models in real-world scenarios.

Key Features:

• Over 90 practical recipes for implementing deep learning solutions.
• Coverage of various neural network architectures and their applications.
• Hands-on guidance for solving real-world AI-driven problems.

Here is the Amazon link.