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Volver a AI for Medical Diagnosis

Opiniones y comentarios de aprendices correspondientes a AI for Medical Diagnosis por parte de deeplearning.ai

4.7
estrellas
1,643 calificaciones
357 reseña

Acerca del Curso

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required! This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare....

Principales reseñas

RK
2 de jul. de 2020

It was a nice course. Though it covers basics. A follow-up advanced specilization can be made. Overall, it's sufficient for beginner for an engineer trying to learn application of AI for medical field

KH
26 de may. de 2020

Throughout this course, I was able to understand the different medical and deep learning terminology used. Definitely a good course to understand the basic of image classification and segmentation!

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351 - 357 de 357 revisiones para AI for Medical Diagnosis

por krishan s

6 de jul. de 2020

Not useful. Probability distributions are not intuitive mostly.

por Sagimbayev Z

10 de jun. de 2020

This course relays on "add one line" code too often.

por Julian S

5 de dic. de 2021

T​he course was quite shallow, and the actual challenges of model selection, training or building appropriate augmentation steps were pre-built and not discussed in any detail.

The coding challenges were using badly outdated package versions, for which documentation does not exist anymore and which do not represent best practice usage of the libraries involved.

O​n top of that, the coding challenges expect a very specific solution, while not considering equivalent implementations as correct (case in point: In the week 3 coding challenge, I used np.transpose where the challenge used np.moveaxis. I prefer transpose since it clearly and explicitly states where _all_ the other axis go, while moveaxis makes that change of state implicitly.)

Finally, the grading of the last coding challenge does not respect the special cases that are explicitly mentioned in the excercise itself. The "standardize" function to be implemented explicitly mentions the possibility of a slice having a zero standard deviation and the pre-coded framework handles this special case correctly. However, if one changes the selection of the slice in the cell before, which the user is encouraged to do, it is possible to obtain an empty slice. The grader expects a unit standard deviation though, without checking this edge case.

T​he shallow content and lackluster excercises, as well as the common mistakes in the presentation videos (sometimes corrected by a "question" popup during playback) do not give the impression this course was prepared well.

por Aliakbar D

28 de jul. de 2020

I have done several of AI courses including the TensorFlow. While the TensorFlow course, gives you a neat and excellent hands on on how to build a network from scratch or implement easily a CNN such as Inception V3, this course make you confused as what sort of aim it follows. Overall confusing and not useful. Though you find some good stuff in the videos but the design and strategy of the course is meaningless.

por Jamal H

19 de ago. de 2021

L​ectures are short, mainly focused on programming details (how to subsample and image or how to calculate an error). The assignments do not help understand the AI part of the medical diagnosis. It can be considered as an intro course for the AI for MD.

por NICOLA F

1 de jun. de 2021

No for medical students. Terrible time loosing

por José M R

5 de may. de 2020

Very basic