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138661975
Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Paperback
Item #:
138661975
XCD 75
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Unlock the transformative power of deep learning with Deep Learning: Python for Data Science, your essential guide to building, training, and deploying advanced deep learning models.
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What Stands Out
Comprehensive Frameworks
Covers major deep learning frameworks like TensorFlow, PyTorch, and Keras, providing a versatile toolkit for developers to create and customize models effectively.
User-Friendly Approach
Designed for data science enthusiasts, this guide simplifies complex concepts and offers practical examples to enhance learning and application in real-world scenarios.
Future-Proof Skills
Equips readers with essential skills in deep learning, ensuring relevance in the rapidly evolving tech landscape and enhancing career opportunities in data science.
Product Details
- 8 x 10 Inch Large Pages - Black and White VersionDeep Learning: Python for Data Science A Guide to Using Python for TensorFlow, PyTorch, and Keras in Creating Deep Learning Model FrameworksUnlock the transformative power of deep learning with Deep Learning: Python for Data Science, your essential guide to building, training, and deploying advanced deep learning models using Python’s leading frameworks—TensorFlow, PyTorch, and Keras. Whether you’re an aspiring data scientist, experienced developer, or technology enthusiast, this book provides a hands-on approach to mastering deep learning techniques crucial for today’s AI-driven world.Inside This Book:Foundations of Deep Learning: Understand neural networks, their evolution from traditional machine learning, and their applications in image recognition, natural language processing, and recommendation systems.Mastering TensorFlow: Dive into TensorFlow’s execution models, tensor operations, and data pipelines. Learn to build and optimize models, leverage GPU support, and enhance performance with tf.function.Harnessing Keras for Simplicity and Power: Utilize Keras to streamline model development. From Sequential models for quick prototypes to the Functional API for complex architectures, gain skills in compiling, training, evaluating, and visualizing models with TensorBoard.Exploring PyTorch’s Flexibility: Transition to PyTorch’s dynamic computation graph and intuitive design. Master tensor operations, define models with torch.nn.Module, implement automatic differentiation, and develop robust training scripts.Advanced Model Architectures: Enhance your models with multi-input/multi-output architectures, custom layers, and modules. Optimize training workflows with data augmentation, normalization, dropout, and batch normalization.Data Preprocessing and Input Pipelines: Effectively handle diverse data types—images, text, and tabular data. Convert raw data into tensors, utilize tf.data and torch.utils.data.Dataset for efficient data handling, and implement strategies for large-scale datasets.Deployment and Serving Models: Transition models to production with TensorFlow Serving and TorchServe. Create REST APIs using Flask or FastAPI, design batch inference pipelines, and optimize infrastructure for low latency and high throughput.For those interested in:zdeep learning, Python for data science, TensorFlow, PyTorch, Keras, neural networks, machine learning, AI models, data preprocessing, model training, model deployment, deep learning frameworks, advanced deep learning, data science guide, practical deep learning, building AI models, training neural networks, deploying deep learning models, TensorFlow tutorials, PyTorch tutorials, Keras tutorials, machine learning with Python, AI development, deep learning applications, image recognition, natural language processing, recommendation systems, GPU acceleration, TensorFlow vs PyTorch, Keras models, deep learning techniques, data science workflows, automated machine learning, deep learning projects, scalable AI models, deep learning optimization, model evaluation, deep learning pipelines, real-world AI, responsible AI, ethical deep learning, deep learning best practices, hands-on deep learning, comprehensive deep learning, multi-framework deep learning, deep learning strategies
| Publisher | Independently published |
| Publication date | 19 May 2025 |
| Language | English |
| Print length | 125 pages |
| ISBN-13 | 979-8284473689 |
| Item weight | 340 g |
| Dimensions | 20.32 x 0.74 x 25.4 cm |
Who Should Buy?
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Aspiring Data Scientists
Individuals looking to develop deep learning skills and utilize Python frameworks for data analysis and machine learning.
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Python Developers
Developers seeking to expand their knowledge into deep learning applications using popular libraries like TensorFlow and PyTorch.
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Students Learning AI
Students and learners interested in artificial intelligence who want a practical guide to implementing deep learning models.
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Beginners in Programming
Users with no programming experience may find the subject matter too complex and challenging to understand.
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Occasional Learners
Casual readers not intending to practice deep learning will not benefit from the practical coding examples provided.
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Non-Technical Professionals
Professionals outside of technical fields may struggle with the advanced terminology and concepts presented in the book.
Product Description
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Customer Questions & Answers
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Question:
Who is this book for?
Answer: It's ideal for aspiring data scientists, experienced developers, and anyone interested in deep learning. -
Question:
What frameworks does the book cover?
Answer: The book focuses on TensorFlow, PyTorch, and Keras. -
Question:
Can I use the techniques learned for real-world applications?
Answer: Absolutely! The book provides practical strategies for building and deploying deep learning models in production.
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XCD 75
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Features & Benefits
- Comprehensive guide to deep learning using Python's top frameworks: TensorFlow, PyTorch, and Keras.
- Hands-on approach tailored for aspiring data scientists, developers, and tech enthusiasts.
- Master neural networks, model training, and advanced deep learning techniques.
- Explore TensorFlow’s execution models, Keras's streamlined development, and PyTorch’s flexibility.
- Learn to preprocess diverse data types and optimize model deployment for production.
- Equipped with practical strategies for building AI-driven applications in today's world.