Master TensorFlow, Keras, and PyTorch for deep learning in AI applications. Learn neural networks, CNNs, RNNs, LSTMs, and GANs through hands-on exercises and real-world projects.
Key Features
- TensorFlow, Keras, and PyTorch for diverse deep learning frameworks
- Neural network concepts with real-world industry relevance
- Cloud and edge AI deployment techniques for scalable solutions
Book Description
Dive into the world of deep learning with this comprehensive guide that bridges theory and practice. From foundational neural networks to advanced architectures like CNNs, RNNs, and Transformers, this book equips you with the tools to build, train, and optimize AI models using TensorFlow, Keras, and PyTorch. Clear explanations of key concepts such as gradient descent, loss functions, and backpropagation are combined with hands-on exercises to ensure practical understanding. Explore cutting-edge AI frameworks, including generative adversarial networks (GANs) and autoencoders, while mastering real-world applications like image classification, text generation, and natural language processing. Detailed chapters cover transfer learning, fine-tuning pretrained models, and deployment strategies for cloud and edge computing. Practical exercises and projects further solidify your skills as you implement AI solutions for diverse challenges. Whether you're deploying AI models on cloud platforms like AWS or optimizing them for edge devices with TensorFlow Lite, this book provides step-by-step guidance. Designed for developers, AI enthusiasts, and data scientists, it balances theoretical depth with actionable insights, making it the ultimate resource for mastering modern deep learning frameworks and advancing your career in AI
What you will learn
- Understand neural network basics
- Build models using TensorFlow and Keras
- Train and optimize PyTorch models
- Apply CNNs for image recognition
- Use RNNs and LSTMs for sequence tasks
- Leverage Transformers in NLP
Who this book is for
This book is for software developers, AI enthusiasts, data scientists, and ML engineers who aim to master deep learning frameworks. A foundational understanding of programming and basic ML concepts is recommended. Ideal for those seeking hands-on experience in real-world AI projects.
