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Natural Language Processing (Text Processing) [Intermediate]

SA

by Super AI Engineer Course
Course level: Intermediate
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Categories Natural Language Processing
Duration 02h 43m 52s
Total Enrolled 22641
Last Update October 24, 2021

Description

อาจารย์ผู้สอน

ดร.กอบกฤตย์ วิริยะยุทธกร

CEO iApp Technology

*** วิชานี้สงวนสิทธิ์เฉพาะผู้สมัครเข้าร่วมโครงการ Super AI Engineer เท่านั้น

สำหรับผู้ที่สมัครโครงการ Super AI Engineer สามารถ Start Course ได้ในวันที่ 10 ตุลาคม 2564 เป็นต้นไป

What Will I Learn?

องค์ความรู้เชิงลึกด้าน Natural Language Processing

Topics for this course

13 Lessons02h 43m 52s

NLP in Deep Learning

EP1 – Outline Course and NLP Transformer00:09:56

EP2 – Feature Representation00:17:06

EP3 – Sequence Models and Seq2Seq Model00:11:22

Transformer

GPT-1

BERT

Other Transformer-based Models

About the instructors

Super AI Engineer Course

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Chawalit

2 years ago

เป็นซีรีส์ยาว ความรู้จัดเต็ม ขอบคุณมากครับ

Free

Target Audience

บุคคลทั่วไปที่สนใจหรือผู้ที่ต้องการทบทวนองค์ความรู้ด้านปัญญาประดิษฐ์

Natural Language Processing (Text Processing) [Intermediate]

Description

What Will I Learn?

Topics for this course

NLP in Deep Learning

EP1 – Outline Course and NLP Transformer00:09:56

EP2 – Feature Representation00:17:06

EP3 – Sequence Models and Seq2Seq Model00:11:22

Transformer

EP4 – Transformer 1 Encoder and Decoder Performance00:13:14

EP5 – Transformer 2 Inside Encoder and Decoder00:13:46

EP6 – Transformer 3 Self-Attention, Residuals and Overall, Output and Training00:10:36

GPT-1

EP7 – GPT-100:12:34

BERT

EP8 – BERT Theory00:15:36

EP9 – BERT Task Specific Methods00:05:33

EP10 – BERT Practical00:09:20

EP11 – BERT Fine Tuning00:17:53

Other Transformer-based Models

EP12 – Other Transformer-based Models and GPT-200:18:33

EP13 – Q&A00:08:30

About the instructors

Super AI Engineer Course

Student Feedback

Tags

Target Audience

Natural Language Processing (Text Processing) [Intermediate]

Description

What Will I Learn?

Topics for this course

NLP in Deep Learning

EP1 – Outline Course and NLP Transformer00:09:56

EP2 – Feature Representation00:17:06

EP3 – Sequence Models and Seq2Seq Model00:11:22

Transformer

EP4 – Transformer 1 Encoder and Decoder Performance00:13:14

EP5 – Transformer 2 Inside Encoder and Decoder00:13:46

EP6 – Transformer 3 Self-Attention, Residuals and Overall, Output and Training00:10:36

GPT-1

EP7 – GPT-100:12:34

BERT

EP8 – BERT Theory00:15:36

EP9 – BERT Task Specific Methods00:05:33

EP10 – BERT Practical00:09:20

EP11 – BERT Fine Tuning00:17:53

Other Transformer-based Models

EP12 – Other Transformer-based Models and GPT-200:18:33

EP13 – Q&A00:08:30

About the instructors

Super AI Engineer Course

Student Feedback

Login

Tags

Target Audience

Login