Deep Learning: Python for Data Science (Book 4): A Guide to using Python for Tensor Flow, PyTorch, & Keras in creating Deep Learning Model Frameworks

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Deep Learning: Python for Data Science (Book 4) 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 Read more

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