Deep Learning using Scikit-Learn, Keras and TensorFlow by Dr.K.Parish Venkata Kumar,Mr.Polepogu Rajesh,Mrs.Vangala Navya Sree,Mr.Sesha Srinivasa Dhanvantri Divi,Dr.Suresh Babu Chandolu,Dr.Jammula Lakshmi Narayana

Deep learning has emerged as one of the most influential areas of artificial intelligence, driving significant advancements in fields such as computer vision, natural language processing, speech recognition, healthcare, finance, and autonomous systems. The availability of powerful open-source frameworks and libraries has made deep learning more accessible, enabling students, researchers, and practitioners to design, train, and deploy intelligent models effectively.
The book Deep Learning using Scikit-Learn, Keras and TensorFlow has been collaboratively authored by a team of academicians and practitioners with diverse expertise in machine learning, artificial intelligence, and data science. The multi-author approach brings together complementary perspectives, ensuring a balanced integration of theoretical concepts, algorithmic understanding, and practical implementation using widely adopted tools.
The book begins by establishing the foundations of machine learning and deep learning, followed by a detailed exploration of the Python ecosystem for data preparation and model development. It systematically introduces classical machine learning techniques using Scikit-Learn before progressing to neural networks and deep learning architectures. The text provides comprehensive coverage of model building, training, evaluation, and optimization using Keras and TensorFlow, emphasizing hands-on learning and real-world relevance.
Special emphasis is placed on modern deep learning architectures such as convolutional neural networks and recurrent neural networks, along with their applications in image processing, text analytics, and time-series analysis. Advanced topics, including transfer learning, model deployment, ethical considerations, and emerging trends in deep learning, are also discussed to provide a holistic understanding of the field.
Each chapter is structured to support effective learning through clear explanations, illustrative diagrams, code examples, and case studies. The content has been aligned with contemporary university curricula and is suitable for both classroom instruction and self-study.
This book is intended for undergraduate and postgraduate students of computer science, artificial intelligence, data science, and related engineering disciplines. It also serves as a valuable reference for researchers and industry professionals seeking practical insights into building deep learning solutions using Scikit-Learn, Keras, and TensorFlow.
The authors sincerely hope that this collaborative effort will empower readers with the skills and confidence required to develop robust deep learning models and apply them responsibly to real-world challenges. Constructive feedback from readers and educators is warmly welcomed and will contribute to the continuous improvement of future editions of this book.