Key Features

• Comprehensive introduction to deep learning concepts and architectures.
• Detailed explanation of neural networks, backpropagation, and activation functions.
• Coverage of convolutional and recurrent neural networks with practical examples.
• Emphasis on real-world applications in image, speech, and natural language processing.
• Hands-on projects and Python code samples for essential algorithms.
  Discussion of optimization techniques and regularization methods.
  Real-world case studies for practical understanding.
  Suitable for students, educators, and professionals in AI and data science.
  Practice questions and review exercises at the end of each chapter.