PyTorch Deep Learning

Maxwell Brooks

/ ISBN 13: 9798315177890

Edité par Amazon Digital Services LLC - Kdp, 2025

New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798315177890

Synopsis :

PyTorch Deep Learning: Build and Deploy Models from CNNs to Multimodal Architectures, LLMs, and Beyond is your ultimate guide to mastering advanced deep learning techniques using PyTorch. Whether you're an experienced practitioner, researcher, or engineer looking to push the boundaries of neural network design, this book is packed with comprehensive, hands-on strategies to design, optimize, and deploy production-ready models.

Unlock the full potential of deep learning with this all-in-one resource that covers:

• Advanced PyTorch Fundamentals:
  Dive deep into PyTorch’s core principles, custom autograd functions, efficient data loading, mixed precision training, and GPU optimization techniques. Perfect for those who want to push beyond the basics and build robust, scalable models.
• State-of-the-Art CNN Architectures:
  Explore advanced CNN models such as ResNet, EfficientNet, and ConvNeXt, and learn how to transfer learn and fine-tune them for real-world tasks like medical imaging and object detection. Understand model interpretability with Grad-CAM and saliency maps.
• Sequence Modeling and LSTM Ensembles:
  Master the art of sequence modeling using LSTMs, GRUs, and attention mechanisms for applications like financial market prediction and real-time chatbots. Learn to build and optimize ensembles for improved forecasting accuracy.
• Transformer and Attention Mechanisms:
  Gain a deep understanding of transformer architectures, self-attention, cross-attention, and how to build custom transformers using PyTorch. Discover transformer optimization techniques to enhance performance and scalability.
• Multimodal Deep Learning:
  Combine visual and textual data seamlessly by building multimodal models. Learn fusion techniques to integrate medical images with patient records, and create systems for tasks such as visual question answering and sentiment analysis.
• Generative Models – GANs, VAEs, and Diffusion Models:
  Develop cutting-edge generative models, including GANs (DCGAN, CycleGAN, StyleGAN), VAEs, and the latest diffusion models for high-fidelity image synthesis. Uncover advanced techniques for image generation and creative AI applications.
• Deployment and Production-Ready Strategies:
  Transition from research to production with robust deployment strategies. Learn how to serialize and optimize models with TorchScript and ONNX, containerize applications with Docker, deploy using Kubernetes, and integrate with cloud services (AWS SageMaker, GCP Vertex AI, Azure ML). Explore real-world deployment projects, including enterprise-level chatbots and real-time object detection systems.
• Ethical AI, Interpretability, and Fairness:
  Navigate the ethical challenges of AI deployment. Gain insights into model interpretability using Grad-CAM, Integrated Gradients, LIME, and SHAP, and learn strategies to detect and mitigate bias in language models and deep learning systems.

Key Features:

• Hands-on, Code-Centric Approach:
  Step-by-step examples and projects, including healthcare multimodal classifiers, enterprise chatbots, and real-time search engines.
• Advanced Optimization Techniques:
  Hyperparameter optimization with Optuna, AutoML, Neural Architecture Search (NAS), and learning rate schedulers (Cosine Annealing, Cyclical LR).
• Scalable Deployment Strategies:
  Learn to deploy models at scale using TorchServe, FastAPI, CI/CD pipelines, and cloud infrastructure.
• Ethical and Regulatory Insights:
  Understand the importance of fairness, transparency, and accountability in AI systems and learn to implement ethical practices in model deployment.

Master the art of deep learning with PyTorch and transform your research and projects into cutting-edge, production-ready AI solutions.

Détails bibliographiques

Titre : PyTorch Deep Learning
Éditeur : Amazon Digital Services LLC - Kdp
Date d'édition : 2025
Reliure : PAP
Etat : New