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تمت ترجمة هذه الصفحة بواسطة Cloud Translation API‏.

لماذا TensorFlow

سواء كنت خبيرًا أو مبتدئًا، فإن TensorFlow عبارة عن منصة شاملة تسهل عليك إنشاء نماذج تعلم الآلة ونشرها.

شاهد الفيديو دراسات الحالة

بناء نموذج سهل

يقدم TensorFlow مستويات متعددة من التجريد حتى تتمكن من اختيار المستوى المناسب لاحتياجاتك. قم ببناء النماذج وتدريبها باستخدام واجهة برمجة تطبيقات Keras عالية المستوى، مما يجعل بدء استخدام TensorFlow والتعلم الآلي أمرًا سهلاً.

إذا كنت بحاجة إلى مزيد من المرونة، فإن التنفيذ المتحمّس يسمح بالتكرار الفوري والتصحيح البديهي. بالنسبة لمهام التدريب الكبيرة على تعلم الآلة، استخدم واجهة برمجة تطبيقات إستراتيجية التوزيع للتدريب الموزع على تكوينات الأجهزة المختلفة دون تغيير تعريف النموذج.

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إنتاج قوي لتعلم الآلة في أي مكان

لقد وفر TensorFlow دائمًا طريقًا مباشرًا للإنتاج. سواء كان ذلك على الخوادم أو الأجهزة الطرفية أو الويب، يتيح لك TensorFlow تدريب نموذجك ونشره بسهولة، بغض النظر عن اللغة أو النظام الأساسي الذي تستخدمه.

استخدم TFX إذا كنت بحاجة إلى خط أنابيب ML للإنتاج الكامل. لتشغيل الاستدلال على الأجهزة المحمولة وأجهزة الحافة، استخدم TensorFlow Lite. تدريب ونشر النماذج في بيئات JavaScript باستخدام TensorFlow.js.

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تجربة قوية للبحث

قم ببناء وتدريب أحدث النماذج دون التضحية بالسرعة أو الأداء. يمنحك TensorFlow المرونة والتحكم باستخدام ميزات مثل Keras Functional API وModel Subclassing API لإنشاء طبولوجيا معقدة. لتسهيل إنشاء النماذج الأولية وتصحيح الأخطاء بسرعة، استخدم التنفيذ المتحمّس.

يدعم TensorFlow أيضًا نظامًا بيئيًا يضم مكتبات ونماذج إضافية قوية للتجربة، بما في ذلك Ragged Tensors وTensorFlow Probability وTensor2Tensor وBERT.

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إير بي إن بي

كوكا كولا

العقل العميق

جنرال إلكتريك للرعاية الصحية

جوجل

شركة انتل

نيرسك

تويتر

تعرف على كيفية عمل التعلم الآلي

هل تريد يومًا أن تعرف كيف تعمل الشبكة العصبية؟ أو ما هي خطوات حل مشكلة تعلم الآلة؟ لا تقلق، لقد قمنا بتغطيتك. فيما يلي نظرة عامة سريعة على أساسيات التعلم الآلي. أو، إذا كنت تبحث عن معلومات أكثر تفصيلاً، فتوجه إلى صفحتنا التعليمية للحصول على محتوى للمبتدئين والمتقدمين.

تعلم تعلم الآلة

مقدمة إلى ML خطوات حل مشكلة ML تشريح الشبكة العصبية تدريب الشبكة العصبية

مقدمة إلى ML

التعلم الآلي هو ممارسة مساعدة البرامج على أداء مهمة دون برمجة أو قواعد واضحة. في برمجة الكمبيوتر التقليدية، يحدد المبرمج القواعد التي يجب أن يستخدمها الكمبيوتر. ومع ذلك، يتطلب تعلم الآلة عقلية مختلفة. يركز تعلم الآلة في العالم الحقيقي على تحليل البيانات أكثر من التركيز على البرمجة. يقدم المبرمجون مجموعة من الأمثلة ويتعلم الكمبيوتر الأنماط من البيانات. يمكنك التفكير في التعلم الآلي على أنه "البرمجة باستخدام البيانات".

خطوات حل مشكلة ML

هناك خطوات متعددة في عملية الحصول على إجابات من البيانات باستخدام تعلم الآلة. للحصول على نظرة عامة خطوة بخطوة، راجع هذا الدليل الذي يعرض سير العمل الكامل لتصنيف النص، ويصف الخطوات المهمة مثل جمع مجموعة بيانات، والتدريب وتقييم النموذج باستخدام TensorFlow.

تشريح الشبكة العصبية

الشبكة العصبية هي نوع من النماذج التي يمكن تدريبها للتعرف على الأنماط. وهي تتألف من طبقات، بما في ذلك طبقات الإدخال والإخراج، وطبقة مخفية واحدة على الأقل. تتعلم الخلايا العصبية في كل طبقة بشكل متزايد تمثيلات مجردة للبيانات. على سبيل المثال، في هذا المخطط المرئي نرى الخلايا العصبية تكتشف الخطوط والأشكال والأنسجة. تتيح هذه التمثيلات (أو الميزات المستفادة) تصنيف البيانات.

تدريب الشبكة العصبية

يتم تدريب الشبكات العصبية عن طريق النسب المتدرج. تبدأ الأوزان في كل طبقة بقيم عشوائية، ويتم تحسينها بشكل متكرر بمرور الوقت لجعل الشبكة أكثر دقة. يتم استخدام دالة الخسارة لتحديد مدى عدم دقة الشبكة، ويتم استخدام إجراء يسمى الانتشار العكسي لتحديد ما إذا كان يجب زيادة كل وزن أو تقليله لتقليل الخسارة.

لماذا TensorFlow

نظام بيئي كامل لمساعدتك على حل مشكلات العالم الحقيقي الصعبة باستخدام التعلم الآلي

بناء نموذج سهل

إنتاج قوي لتعلم الآلة في أي مكان

تجربة قوية للبحث

تعرف على كيفية استخدام الشركات لـ TensorFlow

تعرف على كيفية عمل التعلم الآلي

مقدمة إلى ML

خطوات حل مشكلة ML

تشريح الشبكة العصبية

تدريب الشبكة العصبية

مجتمعنا

يمكنك البناء والنشر والتجربة بسهولة باستخدام TensorFlow