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

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Deep Learning with TensorFlow 2 and Keras

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;

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Machine Learning with Scikit-Learn and TensorFlow

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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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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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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 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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Beginning Deep Learning with TensorFlow

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as;

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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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Python Machine Learning

Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for;

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Beginning Deep Learning with TensorFlow

TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands;

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Python 3 for Machine Learning

related to machine learning Includes separate appendices for regular expressions, Keras, and TensorFlow 2;

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TensorFlow Machine Learning Cookbook

Linear Regression techniques with TensorFlow * Learn SVMs with hands-on recipes * Implement neural networks and improve predictions * Apply;

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TensorFlow 2.0 Quick Start Guide

your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced;

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Python Machine Learning -

updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can;

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Hands-On Deep Learning with Apache Spark

learning with Spark Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras Explore popular deep learning algorithms;

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Machine Learning with scikit-learn Quick Start Guide

learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine;

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HandsOn Unsupervised Learning Using Python

Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find;

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Beginning Anomaly Detection Using Python-Based Deep Learning

deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications;

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Hands-on Scikit-Learn for Machine Learning Applications

machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate;

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning;

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Artificial Intelligence, Machine Learning, and Deep Learning

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;

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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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TensorFlow For Dummies

TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with;

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