Key Features

• Comprehensive introduction to deep learning concepts and architectures.
• Explains neural networks, backpropagation, and activation functions in detail.
• Covers convolutional and recurrent neural networks with practical examples.
• Focus on real-world applications in image, speech, and natural language processing.
• Includes hands-on projects and Python code samples for key algorithms.
  Discusses optimization techniques and regularization methods.
  Real-world case studies for practical understanding.
  Useful for students, educators, and professionals in AI and data science.
  Practice questions and review exercises at the end of each chapter.