جزییات کتاب
«کتاب یادگیری ماشین با تنسورفلو» (Machine Learning With TensorFlow) به مخاطبان خود مبنایی مستحکم از مفاهیم نظری یادگیری ماشین را به همراه تجربه کدنویسی تنسورفلو با پایتون ارائه میکند. مخاطبان با مطالعه این کتاب یادگیری ماشین لرنینگ با پایتون مبانی یادگیری ماشین را ضمن کار با الگوریتمهای پیشبینی کلاسیک، «دستهبندی» (Classification) و «خوشهبندی» (Clustering) میآموزد. در این کتاب، فصلهایی نیر به مفاهیم یادگیری عمیق شامل «خودرمزگذارها» (Autoencoders) و «شبکههای عصبی بازگشتی» (Recurrent Neural Networks) اختصاص داده شده و همچنین، به مبحث یادگیری تقویتی نیز پرداخته شده است.
Summary by khashayarmdr
Summary Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. About the Book Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. What's Inside Matching your tasks to the right machine-learning and deep-learning approachesVisualizing algorithms with TensorBoardUnderstanding and using neural networks About the Reader Written for developers experienced with Python and algebraic concepts like vectors and matrices. About the Author Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics. Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIGA machine-learning odysseyTensorFlow essentialsPART 2 - CORE LEARNING ALGORITHMSLinear regression and beyondA gentle introduction to classificationAutomatically clustering dataHidden Markov models PART 3 - THE NEURAL NETWORK PARADIGMA peek into autoencodersReinforcement learningConvolutional neural networksRecurrent neural networksSequence-to-sequence models for chatbotsUtility landscape