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Step by Step Tutorials on Deep Learning Using Scikit-Learn, Keras, and Tensorflow with Python GUI

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Deep Learning with Applications Using Python

learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text;

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Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-learn

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Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author;

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Python Machine Learning

DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step;

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Deep Learning with TensorFlow 2 and Keras

apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep;

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Python for Probability, Statistics, and Machine Learning

, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance;

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Python for Probability Statistics and Machine Learning

, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance;

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Beginning Anomaly Detection Using Python-Based Deep Learning

statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to;

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Practical Deep Learning for Cloud and Mobile RealWorld AI Computer Vision Projects Using Python, Keras TensorFlow

learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning;

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The Deep Learning Workshop

Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition;

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Python Deep Learning

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book;

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Deep Learning Projects Using TensorFlow 2

Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will;

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Python Machine Learning -

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries;

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Hands-On Machine Learning with Scikit-Learn and TensorFlow

shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author;

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Hands-On Deep Learning for Images with TensorFlow

all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn Build machine learning models;

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Deep Learning with Python

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly;

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Reinforcement Learning

learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book;

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Deep Learning for Computer Vision

their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a;

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Advanced Deep Learning for Engineers and Scientists

effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python;

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Natural Language Processing and Computational Linguistics

computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras;

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Principles and Labs for Deep Learning

, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific;

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Deep Learning with Keras

book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn * Optimize;

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Advanced Deep Learning with Keras

Publisher's Note: This edition from 2018 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python;

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Python for Probability, Statistics, and Machine Learning

same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras;

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Python Data Analytics

computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on;

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Practical Computer Vision Applications Using Deep Learning with CNNs

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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