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دانلود کتاب TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition
دانلود کتاب کتاب آشپزی TensorFlow Machine Learning: بیش از 60 دستور العمل برای ساخت سیستم های یادگیری ماشینی هوشمند با قدرت Python، نسخه دوم
مشخصات کتاب
TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition
ویرایش: Second edition
نویسندگان: McClure. Nick
سری:
ISBN (شابک) : 9781789131680, 178913076X
ناشر: Packt Publishing Limited
سال نشر: 2018
تعداد صفحات: 0
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 8 مگابایت
قیمت کتاب (تومان) : 38,000
کلمات کلیدی مربوط به کتاب کتاب آشپزی TensorFlow Machine Learning: بیش از 60 دستور العمل برای ساخت سیستم های یادگیری ماشینی هوشمند با قدرت Python، نسخه دوم: علوم کامپیوتر، برنامه نویسی، هوش مصنوعی، غیرداستانی
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توجه داشته باشید کتاب کتاب آشپزی TensorFlow Machine Learning: بیش از 60 دستور العمل برای ساخت سیستم های یادگیری ماشینی هوشمند با قدرت Python، نسخه دوم نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب کتاب آشپزی TensorFlow Machine Learning: بیش از 60 دستور العمل برای ساخت سیستم های یادگیری ماشینی هوشمند با قدرت Python، نسخه دوم
ویژگی های کلیدی راهنمای سریع شما برای پیاده سازی TensorFlow در فعالیت های یادگیری ماشینی روزانه خود تکنیک های پیشرفته ای را بیاموزید که دقت و سرعت بیشتری را برای یادگیری ماشین به ارمغان می آورد دانش خود را به نسل دوم یادگیری ماشین ارتقا دهید. کتابخانه نرم افزار منبع برای هوش ماشینی. دستور العمل های مستقل در این کتاب به شما می آموزد که چگونه از TensorFlow برای محاسبات داده های پیچیده استفاده کنید و به شما امکان می دهد عمیق تر بگردید و بینش بیشتری نسبت به داده های خود به دست آورید. شما از طریق دستور العمل های مربوط به مدل های آموزشی، ارزیابی مدل، تجزیه و تحلیل احساسات، تجزیه و تحلیل رگرسیون، تجزیه و تحلیل خوشه بندی، شبکه های عصبی مصنوعی و یادگیری عمیق کار خواهید کرد – که هر کدام از آنها از کتابخانه یادگیری ماشینی Google TensorFlow استفاده می کنند. این راهنما با اصول کتابخانه TensorFlow شروع می شود که شامل آن می شود. متغیرها، ماتریس ها و منابع داده های مختلف. با حرکت رو به جلو، تجربه عملی با تکنیک‌های رگرسیون خطی با TensorFlow خواهید داشت. فصل‌های بعدی مفاهیم مهم سطح بالا مانند شبکه‌های عصبی، CNN، RNN و NLP را پوشش می‌دهند. هنگامی که با اکوسیستم TensorFlow آشنا و راحت هستید، فصل آخر به شما نشان می‌دهد که چگونه آن را به تولید برسانید. آنچه یاد خواهید گرفت تبدیل شوید. آشنایی با اصول کتابخانه یادگیری ماشینی TensorFlow با تکنیک‌های رگرسیون خطی با TensorFlow آشنا شوید SVMs را با دستور العمل‌های عملی بیاموزید پیاده‌سازی شبکه‌های عصبی و بهبود پیش‌بینی‌ها اعمال NLP و تجزیه و تحلیل احساسات در داده‌های خود استاد CNN و RNN از طریق دستور العمل‌های عملی. TensorFlow را وارد تولید کنید. درباره نویسنده نیک مک کلوریس در حال حاضر یک دانشمند ارشد داده در PayScale, Inc. در سیاتل، WA است. قبل از این، او در Zillow and Caesar’s Entertainment کار کرده است. او مدرک خود را در ریاضیات کاربردی از دانشگاه مونتانا و کالج سنت بندیکت و دانشگاه سنت جان گرفت. او علاقه زیادی به یادگیری و حمایت از تجزیه و تحلیل، یادگیری ماشینی و هوش مصنوعی دارد. نیک گاهی اوقات افکار و افکار خود را در وبلاگ خود http://fromdata.org/ یا حساب توییتر خود قرار می دهد: @nfmcclure.
توضیحاتی درمورد کتاب به خارجی
Key Features Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Book DescriptionTensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production About the AuthorNick McClureis currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar’s Entertainment. He got his degrees in Applied Mathematics from the University of Montana and the College of Saint Benedict and Saint John’s University.He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musing on his blog,http://fromdata.org/, or his Twitter account: @nfmcclure.
فهرست مطالب
Cover Title Page Copyright and Credits Dedication Packt Upsell Contributors Table of Contents Preface Chapter 1: Getting Started with TensorFlow Introduction How TensorFlow works Getting ready How to do it... How it works... See also Declaring variables and tensors Getting ready How to do it... How it works... There\'s more... Using placeholders and variables Getting ready How to do it... How it works... There\'s more... Working with matrices Getting ready How to do it... How it works... Declaring operations Getting ready How to do it... How it works... There\'s more... Implementing activation functions Getting ready How to do it... How it works... There\'s more... Working with data sources Getting ready How to do it... How it works... See also Additional resources Getting ready How to do it... Chapter 2: The TensorFlow Way Introduction Operations in a computational graph Getting ready How to do it... How it works... Layering nested operations Getting ready How to do it... How it works... There\'s more... Working with multiple layers Getting ready How to do it... How it works... Implementing loss functions Getting ready How to do it... How it works... There\'s more... Implementing backpropagation Getting ready How to do it... How it works... There\'s more... See also Working with batch and stochastic training Getting ready How to do it... How it works... There\'s more... Combining everything together Getting ready How to do it... How it works... There\'s more... See also Evaluating models Getting ready How to do it... How it works... Chapter 3: Linear Regression Introduction Using the matrix inverse method Getting ready How to do it... How it works... Implementing a decomposition method Getting ready How to do it... How it works... Learning the TensorFlow way of linear regression Getting ready How to do it... How it works... Understanding loss functions in linear regression Getting ready How to do it... How it works... There\'s more... Implementing deming regression Getting ready How to do it... How it works... Implementing lasso and ridge regression Getting ready How to do it... How it works... There\'s more... Implementing elastic net regression Getting ready How to do it... How it works... Implementing logistic regression Getting ready How to do it... How it works... Chapter 4: Support Vector Machines Introduction Working with a linear SVM Getting ready How to do it... How it works... Reduction to linear regression Getting ready How to do it... How it works... Working with kernels in TensorFlow Getting ready How to do it... How it works... There\'s more... Implementing a non-linear SVM Getting ready How to do it... How it works... Implementing a multi-class SVM Getting ready How to do it... How it works... Chapter 5: Nearest-Neighbor Methods Introduction Working with nearest-neighbors Getting ready How to do it... How it works... There\'s more... Working with text based distances Getting ready How to do it... How it works... There\'s more... Computing with mixed distance functions Getting ready How to do it... How it works... There\'s more... Using an address matching example Getting ready How to do it... How it works... Using nearest-neighbors for image recognition Getting ready How to do it... How it works... There\'s more... Chapter 6: Neural Networks Introduction Implementing operational gates Getting ready How to do it... How it works... Working with gates and activation functions Getting ready How to do it... How it works... There\'s more... Implementing a one-layer neural network Getting ready How to do it... How it works... There\'s more... Implementing different layers Getting ready How to do it... How it works... Using a multilayer neural network Getting ready How to do it... How it works... Improving the predictions of linear models Getting ready How to do it How it works... Learning to play Tic Tac Toe Getting ready How to do it... How it works... Chapter 7: Natural Language Processing Introduction Working with bag-of-words embeddings Getting ready How to do it... How it works... There\'s more... Implementing TF-IDF Getting ready How to do it... How it works... There\'s more... Working with Skip-Gram embeddings Getting ready How to do it... How it works... There\'s more... Working with CBOW embeddings Getting ready How to do it... How it works... There\'s more... Making predictions with word2vec Getting ready How to do it... How it works... There\'s more... Using doc2vec for sentiment analysis Getting ready How to do it... How it works... Chapter 8: Convolutional Neural Networks Introduction Implementing a simple CNN Getting ready How to do it... How it works... There\'s more... See also Implementing an advanced CNN Getting ready How to do it... How it works... See also Retraining existing CNN models Getting ready How to do it... How it works... See also Applying stylenet and the neural-style project Getting ready How to do it... How it works... See also Implementing DeepDream Getting ready How to do it... There\'s more... See also Chapter 9: Recurrent Neural Networks Introduction Implementing RNN for spam prediction Getting ready How to do it... How it works... There\'s more... Implementing an LSTM model Getting ready How to do it... How it works... There\'s more... Stacking multiple LSTM layers Getting ready How to do it... How it works... Creating sequence-to-sequence models Getting ready How to do it... How it works... There\'s more... Training a Siamese similarity measure Getting ready How to do it... There\'s more... Chapter 10: Taking TensorFlow to Production Introduction Implementing unit tests Getting ready How it works... Using multiple executors Getting ready How to do it... How it works... There\'s more... Parallelizing TensorFlow Getting ready How to do it... How it works... Taking TensorFlow to production Getting ready How to do it... How it works... An example of productionalizing TensorFlow Getting ready How to do it... How it works... Using TensorFlow Serving Getting ready How to do it... How it works... There\'s more... Chapter 11: More with TensorFlow Introduction Visualizing graphs in TensorBoard Getting ready How to do it... There\'s more... Working with a genetic algorithm Getting ready How to do it... How it works... There\'s more... Clustering using k-means Getting ready How to do it... There\'s more... Solving a system of ordinary differential equations Getting ready How to do it... How it works... See also Using a random forest Getting ready How to do it... How it works... See also Using TensorFlow with Keras Getting ready How to do it... How it works... See also Other Books You May Enjoy Index
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