Hands On Machine Learning With Scikit Learn Keras And Tensorflow

Hands On Machine Learning with Scikit Learn  Keras  and TensorFlow PDF
Author: Aurélien Géron
Publisher: O'Reilly Media
ISBN: 1492032611
Size: 46.46 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 856
View: 126

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Hands On Machine Learning With Scikit Learn And Tensorflow

Hands On Machine Learning with Scikit Learn and TensorFlow PDF
Author: Aurélien Géron
Publisher: "O'Reilly Media, Inc."
ISBN: 1491962267
Size: 59.11 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 574
View: 4512

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

Hands On Machine Learning With Scikit Learn Keras And Tensorflow 2nd Edition

Hands On Machine Learning with Scikit Learn  Keras  and TensorFlow  2nd Edition PDF
Author: Aurélien Géron
Publisher:
ISBN:
Size: 75.60 MB
Format: PDF, ePub
Category :
Languages : en
Pages : 848
View: 7599

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets.

Learn Python

Learn Python PDF
Author: Zed Fast
Publisher: Independently Published
ISBN: 9781670438003
Size: 63.73 MB
Format: PDF
Category :
Languages : en
Pages : 180
View: 4841

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If you want to learn machine learning algorithms from scratch then read more. Artificial Intelligence is everywhere. You are certainly using it every day. One of the popular applications of it is Machine Learning (ML), in which computers, software, and devices perform via cognition similarly to human brain. Machine learning is an important part of personal assistant like Siri, Alexa, Google Now or search engine results or traffic predictions and much more. With this book you will learn fundamentals of machine learning, the underlying challenges with example from open source data sets that you can easily access and get your hands dirty. You will learn: The most popular and widely used machine learning algorithms Fundamentals of machine learning used every day for thousands of algorithms The different stages to create training data set Scikit-Learn library to develop machine learning models using pictures and actual Python code The end to end process of creating and training Neural Network models using the TensorFlow Learn Python includes actual Python code that you can put to practical use. Even if you've never read anything about machine learning you will get a precise tutorial on how to start. Scroll up and buy now to start coding machine learning algorithms in just 4 weeks.

Hands On Unsupervised Learning Using Python

Hands On Unsupervised Learning Using Python PDF
Author: Ankur A. Patel
Publisher: O'Reilly Media
ISBN: 1492035610
Size: 18.80 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 362
View: 2234

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Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Applied Deep Learning With Python

Applied Deep Learning with Python PDF
Author: Alex Galea
Publisher: Packt Publishing Ltd
ISBN: 1789806992
Size: 44.85 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 334
View: 512

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A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.

Practical Machine Learning With Tensorflow 2 0 And Scikit Learn

Practical Machine Learning with TensorFlow 2 0 and Scikit Learn PDF
Author: Samuel Holt
Publisher:
ISBN:
Size: 29.96 MB
Format: PDF
Category :
Languages : en
Pages :
View: 3272

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Get to grips with TensorFlow 2.0 and scikit-learn About This Video Embark on your ML journey using the best machine learning practices and the powerful features of TensorFlow 2.0 and scikit-learn Learn to work with unstructured data, images, and noisy text input, and implement the latest Natural Language Processing models and methods Explore supervised and unsupervised algorithms and put them into practice using mini implementation projects as a basis for real-world applications In Detail Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques? If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data. The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task. By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).

Python Machine Learning From Scratch

Python Machine Learning from Scratch PDF
Author: Daniel Nedal
Publisher: Createspace Independent Publishing Platform
ISBN: 9781724264374
Size: 47.33 MB
Format: PDF, ePub, Docs
Category :
Languages : en
Pages : 130
View: 4625

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***BUY NOW (Will soon return to 20.59) ******Free eBook for customers who purchase the print book from Amazon*** Are you thinking of learning more about Machine Learning using Python? This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and underfitting correctness The Bias-Variance Trade-off Feature Extraction and Selection A Regression Example: Predicting Boston Housing Prices Import Libraries: How to forecast and Predict Popular Classification Algorithms Introduction to K Nearest Neighbors Introduction to Support Vector Machine Example of Clustering Running K-means with Scikit-Learn Introduction to Deep Learning using TensorFlow Deep Learning Compared to Other Machine Learning Approaches Applications of Deep Learning How to run the Neural Network using TensorFlow Cases of Study with Real Data Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected] If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/

Hands On Unsupervised Learning With Python

Hands On Unsupervised Learning with Python PDF
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1789349273
Size: 56.83 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 386
View: 7477

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Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using modern Python libraries Practical examples show you how to implement different machine learning and deep learning techniques Book Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learn Use cluster algorithms to identify and optimize natural groups of data Explore advanced non-linear and hierarchical clustering in action Soft label assignments for fuzzy c-means and Gaussian mixture models Detect anomalies through density estimation Perform principal component analysis using neural network models Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

Practical Deep Learning With Python

Practical Deep Learning with Python PDF
Author: Ron Kneusel
Publisher: No Starch Press
ISBN: 1718500742
Size: 30.85 MB
Format: PDF
Category : Computers
Languages : en
Pages : 464
View: 2454

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"An introduction to machine learning and deep learning for beginners. Covers fundamental concepts before presenting classic machine learning models, neural networks, and modern convolutional neural networks. Includes hands-on Python experiments for each model"--

Machine Learning Avec Scikit Learn

Machine learning avec Scikit Learn PDF
Author: Aurélien Géron
Publisher:
ISBN: 9782100765409
Size: 58.85 MB
Format: PDF, Docs
Category : Artificial intelligence
Languages : fr
Pages : 244
View: 4459

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Cet ouvrage, conçu pour tous ceux qui souhaitent s'initier au Machine Learning (apprentissage automatique) est la traduction de la première partie du best-seller américain Hands-On Machine Learning with Scikit-Learn & TensorFlow. Il ne requiert que peu de connaissances en mathématiques et présente les fondamentaux du Machine Learning d'une façon très pratique à l'aide de Scikit-Learn qui est l'un des frameworks de ML les plus utilisés actuellement. Des exercices corrigés permettent de s'assurer que l'on a assimilé les concepts et que l'on maîtrise les outils. Des compléments en ligne interactifs sous forme de Jupyter notebooks complètent le livre avec des exemples exécutables. Ce premier titre est complété par un second ouvrage intitulé Deep Learning avec TensorFlow.

Hands On Machine Learning For Algorithmic Trading

Hands On Machine Learning for Algorithmic Trading PDF
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
ISBN: 1789342716
Size: 54.99 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 516
View: 2766

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Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.