Deep Learning From Basics To Practice Volume One By Andrew Glassner
- Publisher: COMPUTER SCIENCE
- Availability: In Stock
- SKU: 53159
- Number of Pages: 914
Rs.1,720.00
Rs.2,395.00
Tags: Activation Functions , affordable prices , AI , AI Development , AI Hardware , AI Innovation , Andrew Glassner , Autonomous Driving , Backward Propagation , Basics , best books , best books online , Best Price , best prices , Best Selling Books , best shop , Bias , Book Shop , Book shopping , bookshop , bookshop Multan , bookshop near me , bookshop online , bookshop online Multan , bookshopPakistan , buy online books , CNNs , Computer Vision , Convenient Shopping , Cross-validation , Data Preparation , Deep Learning , digital shopping , Ethics in AI , Forward Propagation , Future Directions , good books , good booksonline , Hands-on Exercises , Healthcare Diagnostics , Hyperparameter Tuning , Image Recognition , Internet Shop , Keras , Layers , Machine Learning , Model Implementation , Natural Language Processing , Neural Networks , Neurons , one stop shop , Online Book Shop , ONLINE BOOKS , Online Books Shop , online books store , Online Bookshop , Online Bookshop Pakistan , online bookstore , online shop , online shopping , Online Shopping Pakistan , OnlineShoppingPakistan , Optimization Techniques , Pakistan Bookshop , PakistanBookshop , PakistanOnlineShopping , Performance Metrics , Practical Guide , Practice , price cut , price-friendly Comprehensive , Privacy , PyTorch , ReasonablePrice , reduced price , Reinforcement Learning , Responsible AI , RNNs , secure shopping , Shopping , ShopSmartPakistan , Speech Recognition , TensorFlow , Theoretical Underpinnings , Time-series Analysis , Training , Understanding AI. , Unsupervised Learning , Virtual Shop , Volume One
Deep Learning From Basics to Practice Volume One by Andrew Glassner provides a thorough exploration of deep learning, starting from foundational concepts and progressing to practical applications. This volume aims to demystify deep learning for beginners while offering insights and techniques valuable to seasoned practitioners. Glassner's approach balances theoretical underpinnings with hands-on exercises, ensuring readers gain both understanding and experience in implementing deep learning models.
Key Points:
1. Introduction to Deep Learning Elaborates on the origins, evolution, and significance of deep learning in modern AI, establishing a foundational understanding for readers.
2. Neural Networks Basics Covers the structure and function of neural networks, including neurons, layers, and the importance of activation functions.
3. Training Neural Networks Discusses the process of training neural networks, including data preparation, forward and backward propagation, and optimization techniques.
4. Deep Learning Frameworks Provides an overview of popular deep learning frameworks such as TensorFlow, PyTorch, and Keras, highlighting their features and use cases.
5. Convolutional Neural Networks (CNNs) Explains the architecture and applications of CNNs, particularly in image recognition and computer vision tasks.
6. Recurrent Neural Networks (RNNs) Focuses on RNNs, their unique ability to process sequential data, and their applications in natural language processing and time-series analysis.
7. Practical Applications Showcases real-world applications of deep learning, including speech recognition, autonomous driving, and healthcare diagnostics.
8. Model Evaluation and Tuning Covers techniques for evaluating and tuning models, including cross-validation, hyperparameter tuning, and performance metrics.
9. Ethical Considerations in AI Addresses the ethical implications of deep learning, such as bias, privacy concerns, and the importance of responsible AI development.
10. Future Trends in Deep Learning Explores emerging trends and future directions in deep learning, including advancements in unsupervised learning, reinforcement learning, and AI hardware.
Conclusion
In "Deep Learning From Basics to Practice Volume One," Andrew Glassner offers a comprehensive guide that bridges the gap between theory and practice. By the end of this volume, readers will not only understand the core principles of deep learning but also be equipped to apply these concepts in real-world scenarios, paving the way for further exploration and innovation in the field.
════ ⋆★⋆ ════
Writer ✤ Andrew Glassner