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the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep;
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Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key Features Learn how to define your own;
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effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python;
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Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use;
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Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases;
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Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch;
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Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge;
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deep learning models with fewer and smarter implementations Explore the PyTorch framework for model explainability and to bring transparency;
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-based neural networks Who this book is forThis PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone;
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differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised;
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Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's;
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and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and;
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and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and;
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started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch;
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deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll;
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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates;
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Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key Features Learn how you can speed up the deep;
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how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a;
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cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Deploying computer vision, audio, and;
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machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be;
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spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch;
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tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible, and;
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fundamentals of deep learning and neural networks Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text;
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modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and;
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, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven;
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