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Tại sao TensorFlow

Cho dù bạn là chuyên gia hay người mới bắt đầu, TensorFlow là nền tảng toàn diện giúp bạn dễ dàng xây dựng và triển khai các mô hình ML.

Toàn bộ hệ sinh thái giúp bạn giải quyết các vấn đề thực tế đầy thách thức bằng học máy

Xây dựng mô hình dễ dàng

TensorFlow cung cấp nhiều cấp độ trừu tượng để bạn có thể chọn cấp độ phù hợp với nhu cầu của mình. Xây dựng và đào tạo các mô hình bằng cách sử dụng API Keras cấp cao, giúp việc bắt đầu với TensorFlow và machine learning trở nên dễ dàng.

Nếu bạn cần linh hoạt hơn, khả năng thực thi háo hức cho phép lặp lại ngay lập tức và gỡ lỗi trực quan. Đối với các nhiệm vụ đào tạo ML lớn, hãy sử dụng API Chiến lược phân phối để đào tạo phân tán trên các cấu hình phần cứng khác nhau mà không thay đổi định nghĩa mô hình.

Sản xuất ML mạnh mẽ ở mọi nơi

TensorFlow luôn cung cấp con đường trực tiếp đến sản xuất. Cho dù đó là trên máy chủ, thiết bị biên hay trên web, TensorFlow cho phép bạn đào tạo và triển khai mô hình của mình một cách dễ dàng, bất kể bạn sử dụng ngôn ngữ hay nền tảng nào.

Sử dụng TFX nếu bạn cần một quy trình ML sản xuất đầy đủ. Để chạy suy luận trên thiết bị di động và thiết bị biên, hãy sử dụng TensorFlow Lite. Huấn luyện và triển khai các mô hình trong môi trường JavaScript bằng TensorFlow.js.

Thử nghiệm mạnh mẽ cho nghiên cứu

Xây dựng và đào tạo các mô hình tiên tiến nhất mà không làm giảm tốc độ hoặc hiệu suất. TensorFlow mang đến cho bạn sự linh hoạt và khả năng kiểm soát với các tính năng như API chức năng Keras và API phân lớp mô hình để tạo các cấu trúc liên kết phức tạp. Để tạo mẫu dễ dàng và gỡ lỗi nhanh, hãy sử dụng tính năng thực thi háo hức.

TensorFlow cũng hỗ trợ một hệ sinh thái gồm các thư viện và mô hình tiện ích bổ sung mạnh mẽ để thử nghiệm, bao gồm Ragged Tensors, TensorFlow Probability, Tensor2Tensor và BERT.

Xem cách các công ty đang sử dụng TensorFlow

Airbnb
Cô-ca Cô-la
Tâm trí sâu sắc
GE Healthcare
Google
Intel
NERSC
Twitter

Tìm hiểu cách hoạt động của máy học

Bạn có bao giờ muốn biết mạng lưới thần kinh hoạt động như thế nào không? Hoặc các bước để giải quyết vấn đề ML là gì? Đừng lo lắng, chúng tôi đã bảo vệ bạn. Dưới đây là tổng quan nhanh về các nguyên tắc cơ bản của học máy. Hoặc, nếu bạn đang tìm kiếm thông tin chuyên sâu hơn, hãy truy cập trang giáo dục của chúng tôi để biết nội dung dành cho người mới bắt đầu và nâng cao.

Giới thiệu về ML

Học máy là phương pháp giúp phần mềm thực hiện một nhiệm vụ mà không cần lập trình hoặc quy tắc rõ ràng. Với lập trình máy tính truyền thống, lập trình viên chỉ định các quy tắc mà máy tính nên sử dụng. Tuy nhiên, ML đòi hỏi một tư duy khác. ML trong thế giới thực tập trung nhiều vào phân tích dữ liệu hơn là mã hóa. Các lập trình viên cung cấp một tập hợp các ví dụ và máy tính học các mẫu từ dữ liệu. Bạn có thể coi học máy là “lập trình với dữ liệu”.

Các bước giải quyết vấn đề ML

Có nhiều bước trong quy trình nhận câu trả lời từ dữ liệu bằng ML. Để biết tổng quan từng bước, hãy xem hướng dẫn này hiển thị quy trình làm việc hoàn chỉnh để phân loại văn bản và mô tả các bước quan trọng như thu thập tập dữ liệu cũng như đào tạo và đánh giá mô hình bằng TensorFlow.

Giải phẫu mạng lưới thần kinh

Mạng lưới thần kinh là một loại mô hình có thể được đào tạo để nhận dạng các mẫu. Nó bao gồm các lớp, bao gồm các lớp đầu vào và đầu ra, và ít nhất một lớp ẩn . Các nơ-ron trong mỗi lớp học cách biểu diễn dữ liệu ngày càng trừu tượng. Ví dụ, trong sơ đồ trực quan này, chúng ta thấy các tế bào thần kinh phát hiện các đường, hình dạng và kết cấu. Những biểu diễn này (hoặc các tính năng đã học) giúp phân loại dữ liệu.

Huấn luyện mạng lưới thần kinh

Mạng lưới thần kinh được đào tạo bằng cách giảm độ dốc. Các trọng số trong mỗi lớp bắt đầu bằng các giá trị ngẫu nhiên và các giá trị này được cải thiện lặp đi lặp lại theo thời gian để làm cho mạng chính xác hơn. Hàm mất mát được sử dụng để định lượng mức độ không chính xác của mạng và quy trình gọi là lan truyền ngược được sử dụng để xác định xem mỗi trọng số nên tăng hay giảm để giảm tổn thất.

cộng đồng của chúng tôi

Cộng đồng TensorFlow là một nhóm tích cực gồm các nhà phát triển, nhà nghiên cứu, người có tầm nhìn xa, người mày mò và người giải quyết vấn đề. Cánh cửa luôn rộng mở để đóng góp, hợp tác và chia sẻ ý tưởng của bạn.

,

Tại sao TensorFlow

Cho dù bạn là chuyên gia hay người mới bắt đầu, TensorFlow là nền tảng toàn diện giúp bạn dễ dàng xây dựng và triển khai các mô hình ML.

Toàn bộ hệ sinh thái giúp bạn giải quyết các vấn đề thực tế đầy thách thức bằng học máy

Xây dựng mô hình dễ dàng

TensorFlow cung cấp nhiều cấp độ trừu tượng để bạn có thể chọn cấp độ phù hợp với nhu cầu của mình. Xây dựng và đào tạo các mô hình bằng cách sử dụng API Keras cấp cao, giúp việc bắt đầu với TensorFlow và machine learning trở nên dễ dàng.

Nếu bạn cần linh hoạt hơn, khả năng thực thi háo hức cho phép lặp lại ngay lập tức và gỡ lỗi trực quan. Đối với các nhiệm vụ đào tạo ML lớn, hãy sử dụng API Chiến lược phân phối để đào tạo phân tán trên các cấu hình phần cứng khác nhau mà không thay đổi định nghĩa mô hình.

Sản xuất ML mạnh mẽ ở mọi nơi

TensorFlow luôn cung cấp con đường trực tiếp đến sản xuất. Cho dù đó là trên máy chủ, thiết bị biên hay trên web, TensorFlow cho phép bạn đào tạo và triển khai mô hình của mình một cách dễ dàng, bất kể bạn sử dụng ngôn ngữ hay nền tảng nào.

Sử dụng TFX nếu bạn cần một quy trình ML sản xuất đầy đủ. Để chạy suy luận trên thiết bị di động và thiết bị biên, hãy sử dụng TensorFlow Lite. Huấn luyện và triển khai các mô hình trong môi trường JavaScript bằng TensorFlow.js.

Thử nghiệm mạnh mẽ cho nghiên cứu

Xây dựng và đào tạo các mô hình tiên tiến nhất mà không làm giảm tốc độ hoặc hiệu suất. TensorFlow mang đến cho bạn sự linh hoạt và khả năng kiểm soát với các tính năng như API chức năng Keras và API phân lớp mô hình để tạo các cấu trúc liên kết phức tạp. Để tạo mẫu dễ dàng và gỡ lỗi nhanh, hãy sử dụng tính năng thực thi háo hức.

TensorFlow cũng hỗ trợ một hệ sinh thái gồm các thư viện và mô hình tiện ích bổ sung mạnh mẽ để thử nghiệm, bao gồm Ragged Tensors, TensorFlow Probability, Tensor2Tensor và BERT.

Xem cách các công ty đang sử dụng TensorFlow

Airbnb
Cô-ca Cô-la
Tâm trí sâu sắc
GE Healthcare
Google
Intel
NERSC
Twitter

Tìm hiểu cách hoạt động của máy học

Bạn có bao giờ muốn biết mạng lưới thần kinh hoạt động như thế nào không? Hoặc các bước để giải quyết vấn đề ML là gì? Đừng lo lắng, chúng tôi đã bảo vệ bạn. Dưới đây là tổng quan nhanh về các nguyên tắc cơ bản của học máy. Hoặc, nếu bạn đang tìm kiếm thông tin chuyên sâu hơn, hãy truy cập trang giáo dục của chúng tôi để biết nội dung dành cho người mới bắt đầu và nâng cao.

Giới thiệu về ML

Học máy là phương pháp giúp phần mềm thực hiện một nhiệm vụ mà không cần lập trình hoặc quy tắc rõ ràng. Với lập trình máy tính truyền thống, lập trình viên chỉ định các quy tắc mà máy tính nên sử dụng. Tuy nhiên, ML đòi hỏi một tư duy khác. ML trong thế giới thực tập trung nhiều vào phân tích dữ liệu hơn là mã hóa. Các lập trình viên cung cấp một tập hợp các ví dụ và máy tính học các mẫu từ dữ liệu. Bạn có thể coi học máy là “lập trình với dữ liệu”.

Các bước giải quyết vấn đề ML

Có nhiều bước trong quy trình nhận câu trả lời từ dữ liệu bằng ML. Để biết tổng quan từng bước, hãy xem hướng dẫn này hiển thị quy trình làm việc hoàn chỉnh để phân loại văn bản và mô tả các bước quan trọng như thu thập tập dữ liệu cũng như đào tạo và đánh giá mô hình bằng TensorFlow.

Giải phẫu mạng lưới thần kinh

Mạng lưới thần kinh là một loại mô hình có thể được đào tạo để nhận dạng các mẫu. Nó bao gồm các lớp, bao gồm các lớp đầu vào và đầu ra, và ít nhất một lớp ẩn . Các nơ-ron trong mỗi lớp học cách biểu diễn dữ liệu ngày càng trừu tượng. Ví dụ, trong sơ đồ trực quan này, chúng ta thấy các tế bào thần kinh phát hiện các đường, hình dạng và kết cấu. Những biểu diễn này (hoặc các tính năng đã học) giúp phân loại dữ liệu.

Huấn luyện mạng lưới thần kinh

Mạng lưới thần kinh được đào tạo bằng cách giảm độ dốc. Các trọng số trong mỗi lớp bắt đầu bằng các giá trị ngẫu nhiên và các giá trị này được cải thiện lặp đi lặp lại theo thời gian để làm cho mạng chính xác hơn. Hàm mất mát được sử dụng để định lượng mức độ không chính xác của mạng và quy trình gọi là lan truyền ngược được sử dụng để xác định xem mỗi trọng số nên tăng hay giảm để giảm tổn thất.

cộng đồng của chúng tôi

Cộng đồng TensorFlow là một nhóm tích cực gồm các nhà phát triển, nhà nghiên cứu, người có tầm nhìn xa, người mày mò và người giải quyết vấn đề. Cánh cửa luôn rộng mở để đóng góp, hợp tác và chia sẻ ý tưởng của bạn.