Acerca de este curso: You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
Para quién es esta clase: Pre-requisites: - This course is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and prioritization for their work. - It is recommended that you take course one and two of this specialization (Neural Networks and Deep Learning, and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization) prior to beginning this course.
Enseñado por: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Enseñado por: Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Enseñado por: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Información básica | Curso 3 de 5 en Deep Learning Specialization |
| Nivel | Beginner |
| Compromiso | 2 weeks of study, 3-4 hours/week |
| Idioma | English, Subtítulos: Korean, Chinese (Traditional), Chinese (Simplified) |
| Requerimientos de hardware | None currently, unless you'd like to download Jupyter Notebooks locally for offline work. |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Vídeo: Orthogonalization
- Vídeo: Single number evaluation metric
- Vídeo: Satisficing and Optimizing metric
- Vídeo: Train/dev/test distributions
- Vídeo: Size of the dev and test sets
- Vídeo: When to change dev/test sets and metrics
- Vídeo: Why human-level performance?
- Vídeo: Avoidable bias
- Vídeo: Understanding human-level performance
- Vídeo: Surpassing human-level performance
- Vídeo: Improving your model performance
- Reading: Machine Learning flight simulator
- Vídeo: Andrej Karpathy interview
- Vídeo: Cleaning up incorrectly labeled data
- Vídeo: Build your first system quickly, then iterate
- Vídeo: Training and testing on different distributions
- Vídeo: Bias and Variance with mismatched data distributions
- Vídeo: Addressing data mismatch
- Vídeo: Transfer learning
- Vídeo: Multi-task learning
- Vídeo: What is end-to-end deep learning?
- Vídeo: Whether to use end-to-end deep learning
- Vídeo: Ruslan Salakhutdinov interview
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Good Knowledge :)
An amazing course covering those industry relevant aspects of deep learning and machine learning which not many courses do.
Great course! Very practical and a great resource for becoming a machine learning product manager.
Was a good course with a lot of useful tips that I am sure I am able to use in my job as a data scientist. However, I would've liked if there were a few more hands-on examples (e.g. using jupyter) to really drives these concepts more home.