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Deep Learning Models explored with help of Python Programming

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Hands-On One-shot Learning with Python

learning process with one-shot learning Use Python and PyTorch to build state-of-the-art one-shot learning models Explore architectures such as;

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Deep Learning for Beginners

content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning;

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Deep Learning Essentials

Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with;

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The The Reinforcement Learning Workshop

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and;

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Python Machine Learning

Inside this book you will find all the basic notions to start with Python and all the programming concepts to build machine learning;

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Introduction to Deep Learning

, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming;

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Data Science Solutions with Python

learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics;

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Python Machine Learning Cookbook

approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful;

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Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with;

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Python for Probability, Statistics, and Machine Learning

is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python;

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Python for Probability Statistics and Machine Learning

is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python;

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Python Deep Learning

foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across;

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Deep Learning for Computer Vision

applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and;

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Tensorflow 2

focus of this book is on basic programming instructions used in machine learning and deep learning. Includes companion files with source code;

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Hands-On GPU Computing with Python

will learn, by example, how to perform GPU programming with Python, and you'll look at using integrations such as PyCUDA, PyOpenCL, CuPy and;

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Advanced Deep Learning with Python

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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Artificial Intelligence with Python Cookbook

cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively;

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State-of-the-Art Deep Learning Models in TensorFlow

feed state-of-the-art deep learning models Create pipelined state-of-the-art deep learning models with clean and reliable Python code;

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Python Machine Learning

Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;

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Machine Learning in Cognitive IoT

Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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Hands-On Reinforcement Learning with Python

intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the;

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Hands-On Convolutional Neural Networks with TensorFlow

learning concepts, linear algebra and Python will help.;

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Practical Deep Learning

Deep Learning for Complete Beginners: A Python-Based Introduction is for complete beginners in machine learning. It introduces fundamental;

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Machine Learning Concepts with Python and the Jupyter Notebook Environment

in working with Python in Jupyter Notebooks, you'll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the;

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Advanced Deep Learning for Engineers and Scientists

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time;

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