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این صفحه به‌وسیله ‏Cloud Translation API‏ ترجمه شده است.

چرا تنسورفلو

چه یک متخصص یا مبتدی باشید، TensorFlow یک پلت فرم سرتاسری است که ساخت و استقرار مدل‌های ML را برای شما آسان می‌کند.

فیلم را ببینید مطالعات موردی

ساخت مدل آسان

TensorFlow چندین سطح از انتزاع را ارائه می دهد تا بتوانید متناسب با نیاز خود یکی را انتخاب کنید. با استفاده از Keras API سطح بالا، مدل‌هایی بسازید و آموزش دهید، که شروع کار با TensorFlow و یادگیری ماشین را آسان می‌کند.

اگر به انعطاف بیشتری نیاز دارید، اجرای مشتاقانه امکان تکرار فوری و اشکال زدایی بصری را فراهم می کند. برای کارهای بزرگ آموزش ML، از Distribution Strategy API برای آموزش توزیع شده روی پیکربندی های سخت افزاری مختلف بدون تغییر تعریف مدل استفاده کنید.

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تولید قوی ML در هر کجا

TensorFlow همیشه یک مسیر مستقیم برای تولید فراهم کرده است. خواه روی سرورها، دستگاه‌های لبه‌ای یا وب باشد، TensorFlow به شما امکان می‌دهد بدون توجه به زبان یا پلتفرمی که استفاده می‌کنید، مدل خود را به راحتی آموزش داده و اجرا کنید.

اگر به خط لوله ML تولید کامل نیاز دارید از TFX استفاده کنید. برای اجرای استنتاج بر روی دستگاه های تلفن همراه و لبه، از TensorFlow Lite استفاده کنید. آموزش و استقرار مدل ها در محیط های جاوا اسکریپت با استفاده از TensorFlow.js.

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آزمایش قدرتمند برای تحقیق

مدل های پیشرفته را بدون کاهش سرعت و عملکرد بسازید و آموزش دهید. TensorFlow با ویژگی هایی مانند Keras Functional API و Model Subclassing API برای ایجاد توپولوژی های پیچیده به شما انعطاف پذیری و کنترل می دهد. برای نمونه سازی آسان و اشکال زدایی سریع، از اجرای مشتاقانه استفاده کنید.

TensorFlow همچنین از اکوسیستمی از کتابخانه‌ها و مدل‌های افزودنی قدرتمند برای آزمایش، از جمله Ragged Tensor، TensorFlow Probability، Tensor2Tensor و BERT پشتیبانی می‌کند.

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Airbnb

کوکاکولا

Deepmind

GE Healthcare

گوگل

اینتل

NERSC

توییتر

یاد بگیرید که یادگیری ماشین چگونه کار می کند

آیا تا به حال می خواهید بدانید که یک شبکه عصبی چگونه کار می کند؟ یا مراحل حل مشکل ML چیست؟ نگران نباشید، ما شما را تحت پوشش قرار داده ایم. در زیر مروری سریع بر اصول یادگیری ماشینی است. یا اگر به دنبال اطلاعات عمیق تری هستید، برای مطالب مبتدی و پیشرفته به صفحه آموزش ما سر بزنید.

ML را یاد بگیرید

مقدمه ای بر ML مراحل حل مشکل ML آناتومی یک شبکه عصبی آموزش شبکه عصبی

مقدمه ای بر ML

یادگیری ماشینی تمرین کمک به نرم افزار برای انجام یک کار بدون برنامه نویسی یا قوانین صریح است. با برنامه نویسی سنتی کامپیوتر، یک برنامه نویس قوانینی را مشخص می کند که کامپیوتر باید از آنها استفاده کند. با این حال، ML نیاز به یک طرز فکر متفاوت دارد. دنیای واقعی ML بسیار بیشتر بر تجزیه و تحلیل داده ها تمرکز می کند تا کدنویسی. برنامه نویسان مجموعه ای از مثال ها را ارائه می دهند و کامپیوتر الگوها را از داده ها یاد می گیرد. شما می توانید یادگیری ماشین را به عنوان "برنامه نویسی با داده" در نظر بگیرید.

مراحل حل مشکل ML

مراحل متعددی در فرآیند دریافت پاسخ از داده ها با استفاده از ML وجود دارد. برای یک نمای کلی گام به گام، این راهنما را بررسی کنید که گردش کار کامل را برای طبقه بندی متن نشان می دهد و مراحل مهمی مانند جمع آوری مجموعه داده و آموزش و ارزیابی یک مدل با TensorFlow را شرح می دهد.

آناتومی یک شبکه عصبی

شبکه عصبی نوعی مدل است که می تواند برای تشخیص الگوها آموزش ببیند. این لایه از لایه ها شامل لایه های ورودی و خروجی و حداقل یک لایه پنهان تشکیل شده است. نورون ها در هر لایه نمایش های انتزاعی فزاینده ای از داده ها را یاد می گیرند. به عنوان مثال، در این نمودار بصری، نورون ها را می بینیم که خطوط، اشکال و بافت ها را تشخیص می دهند. این نمایش ها (یا ویژگی های آموخته شده) طبقه بندی داده ها را ممکن می کند.

آموزش شبکه عصبی

شبکه های عصبی با نزول گرادیان آموزش داده می شوند. وزن‌ها در هر لایه با مقادیر تصادفی شروع می‌شوند و این مقادیر در طول زمان به طور مکرر بهبود می‌یابند تا شبکه دقیق‌تر شود. از یک تابع اتلاف برای تعیین کمیت نادرست بودن شبکه استفاده می شود و از روشی به نام پس انتشار برای تعیین اینکه آیا هر وزن باید برای کاهش تلفات افزایش یا کاهش یابد استفاده می شود.

چرا تنسورفلو

یک اکوسیستم کامل برای کمک به حل مشکلات چالش برانگیز دنیای واقعی با یادگیری ماشین

ساخت مدل آسان

تولید قوی ML در هر کجا

آزمایش قدرتمند برای تحقیق

ببینید شرکت‌ها چگونه از TensorFlow استفاده می‌کنند

یاد بگیرید که یادگیری ماشین چگونه کار می کند

مقدمه ای بر ML

مراحل حل مشکل ML

آناتومی یک شبکه عصبی

آموزش شبکه عصبی

جامعه ی ما

با TensorFlow به راحتی بسازید، مستقر کنید و آزمایش کنید