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Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using Python Follow a deep learning;
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Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer;
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vision tasks using TensorFlow. The book begins by taking you through the basics of deep learning for computer vision, along with covering;
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, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both;
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Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for;
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Create computer vision and deep learning web applications Who this book is forThis book is designed for computer vision developers, engineers;
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. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural;
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Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition;
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major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and;
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networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and;
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kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to;
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Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and;
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machine learning for building intelligent computer vision applications with OpenCV 4. What you will learn Understand the core machine learning;
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something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite;
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This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;
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This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;
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vision algorithms and their applications using PyTorch. The book begins with the fundamentals of computer vision: convolutional neural nets;
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computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge;
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neural networks (ANNs) Handle computer vision problems with convolutional neural networks (CNNs) Perform language translation tasks using;
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Explore the potential of deep learning techniques in computer vision applications using the Python ecosystem, and build real-time systems;
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Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the;
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analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network;
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time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using;
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time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real;
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, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL;
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Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;
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subject. See an interview with the author explaining his approach to teaching and learning computer vision - http;
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