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Avis et commentaires pour d'étudiants pour Applied AI with DeepLearning par IBM

4.4
étoiles
954 évaluations
164 avis

À propos du cours

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....

Meilleurs avis

SS
23 oct. 2020

I learned many things from this course. However, I think in some points it could have been instructed much better. But all in all, it is a very worthy course for the price offered. Thanks a lot!

RC
25 avr. 2018

It was really great learning with coursera and I loved the course. The way faculty teaches here is just awesome as they are very much clear and helped a lot while learning this coursea

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101 - 125 sur 164 Avis pour Applied AI with DeepLearning

par Scott L

11 avr. 2020

This class had some interesting information, and some lectures provided additional insight into the world of AI and deep learning, but more often than not I found it to more be a showcase for the ibm platform(not a bad thing and possibly good for people already working in the field). So overall I would say this is just above average, I would give it a 3.5 if I could.

par Naveen M N S

18 févr. 2018

Very hands-on course. Enjoyed the width of problems that were solved. IBM cloud seems irresistible. Certain sections of the course are too fast. For such sections it will be better if the notebook links are provided in the video/description itself.

par Filip G

9 oct. 2019

Nice course with lots of practical examples. Course is delivered by multiple tutors with different styles and level of detail. Overall good introductory course into neural networks, scaling and deployment.

par Omkar G

31 mai 2020

The content of the course gives an idea of several techniques of deep learning. But The concepts ain't explained completely here. Though assignments can be helpful for better understanding...

par Giovani F M

23 janv. 2020

I've learned a lot from this course. I've very much the Time Series Forecasting Section Explanation. The notebook is detailed and the concepts very well discussed.

par Dmitry B

11 janv. 2019

This course is packed with info on different deep learning techniques and libraries. Not all of them can be found in exercises though.

par Muhammad S u

14 mars 2020

Since they are updating the module, still LSTM and CNN were taught extremely well. I am eagerly waiting for the updated materials :)

par Saurabh W

12 mars 2018

One of the great course from IBM Watson .Really one should take this one if intrested in Deep Learning.

par Thomas B

21 mars 2020

This is a good course with good introductory material that covers a broad range of topics.

par Chandan C

9 févr. 2020

Exercises let me explore the topic further which was very helpful for my learning

par Mrutyunjaya S Y

18 juil. 2020

This course gives you a overall concepts of AI with DeepLearning ...Nice course

par Sourastra N

25 juil. 2019

The course needs to allow the students to build their own model.

par Dmitry G

19 juil. 2018

Concise intro to much needed big data machine learning solutions

par Victor d O

9 janv. 2019

I think we need in this module more pratical assignments.

par PRASHANT K R

7 juin 2018

very nice course it gives more insight to deep learning.

par Jair M

22 mai 2019

Some videos are missing, but anyway is a great course

par Amalka W

29 déc. 2019

Course covers scalerble deep learning concepts

par Andrey O

7 sept. 2018

Part with DeepLearning4J is not very good...

par Deleted A

30 juil. 2019

Really Helpful course for AI Enthusiasts

par Mobassir H

22 avr. 2020

pytorch instructor was the best <3

par Valerio N

27 mars 2019

Very Complete course.

par Aarti Y

9 avr. 2018

It was nice

par Tobias H

26 août 2018

n/a

par Pierre-Matthieu P

30 nov. 2019

I was pretty disppointed overall.

Pros : we see many types of tools and get to use some of them in the programming assignments. I feel like I now have a general knowledge of the field. I particularly liked the aspects of scaling and deploying models in production.

Cons : This honestly feels more like a rough draft than a finished and polished course. I would have liked a consolidated overview of all these tools, their pros and cons, etc. Some tools and techniques were explained in literaly 15 min(!) and in some cases simply walked through a tutorial from the tool's website (!!). A programming assignment was broken through not being updated for more recent spark versions. Some videos mentioned a non-existent programming assignment (I assume they were reused from an internal IBM training session), etc. The comparison with say Andrew Ng's course on ML is cruel.

par Appan P

7 juin 2020

Even though this course covers quite a bit of breath - in terms of implementation frameworks, there is scope of improving the presentation material. It will help a lot if the neural network models and the data transformations are explained using pictures.

Also, the one of the videos in the sequence of videos on LSTM for time-series forecasting (week3) talks about comparing performance of MSE and MAE but I could not find any such video on performance comparison.

Also, the assignments are quite simple and wish they had more steps for the student to "fill-up".

There is not much info on deploying the model and online evaluation of its performance. At least one video on how to do it in IBM data cloud will be helpful.