Deep Learning with Python: From Basics to Advanced
Deep Learning with Python: From Basics to Advanced by Dargslan Publishing
English | December 20, 2024 | ISBN: N/A | ASIN: B0DR6B73X1 | 315 pages | EPUB | 0.49 Mb
English | December 20, 2024 | ISBN: N/A | ASIN: B0DR6B73X1 | 315 pages | EPUB | 0.49 Mb
"Deep Learning with Python: From Basics to Advanced" is your comprehensive guide to mastering one of the most transformative technologies in artificial intelligence. This book offers a balanced blend of theory and practice, making complex concepts accessible to beginners while providing in-depth insights for more experienced practitioners.
Key Features:
- Comprehensive coverage from foundational concepts to advanced techniques
- Hands-on approach with practical coding examples using Python
- Exploration of popular deep learning libraries: TensorFlow, PyTorch, and Keras
- Step-by-step guidance for building and training neural networks
- Insights into cutting-edge architectures like CNNs and RNNs
- Real-world applications in computer vision, NLP, and reinforcement learning
- Ethical considerations and future trends in deep learning
What You'll Learn:
- Fundamentals of Deep Learning: Dive into the core principles that set deep learning apart from traditional machine learning. Understand the building blocks of neural networks and how they process information.
- Python for Deep Learning: Get hands-on experience with essential Python libraries. Learn to leverage TensorFlow, PyTorch, and Keras to build powerful deep learning models efficiently.
- Building Neural Networks from Scratch: Gain a deep understanding of neural network architecture by implementing them from the ground up. This foundational knowledge will empower you to customize and optimize models for various tasks.
- Advanced Architectures: Explore state-of-the-art models like Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data analysis. Understand their inner workings and applications.
- Training and Optimization: Master techniques for efficient model training, including data preprocessing, hyperparameter tuning, and regularization methods to prevent overfitting.
- Transfer Learning and Fine-tuning: Learn how to leverage pre-trained models to solve new problems with limited data, significantly reducing training time and improving model performance.
- Deployment and Production: Discover best practices for deploying deep learning models in real-world environments. Understand the challenges of scaling and maintaining models in production.
- Practical Applications: Apply your skills to solve real-world problems in computer vision, natural language processing, and reinforcement learning. Work through end-to-end projects that simulate industry scenarios.
- Ethical Considerations: Explore the ethical implications of deep learning technologies and learn about responsible AI development practices.
- Future Trends: Get insights into emerging trends and future directions in deep learning research and applications.
- Structured Learning Path: The book follows a carefully designed curriculum that ensures a smooth progression from basics to advanced topics.
- Code-First Approach: Learn by doing with numerous coding exercises and projects that reinforce theoretical concepts.
- Industry Relevance: Gain skills that are highly sought after in today's job market, with examples drawn from real-world applications.
- Accessibility
- Comprehensive Coverage