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

4.4
étoiles
858 évaluations
148 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

RC

Apr 26, 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

RK

Jun 14, 2020

The course was amazing however I'm yet to receive my badge from IBM even after completing the course. Would really appreciate if Coursera support could assist me with this.

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101 - 125 sur 148 Avis pour Applied AI with DeepLearning

par Thomas B

Mar 21, 2020

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

par Chandan C

Feb 09, 2020

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

par Sourastra N

Jul 26, 2019

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

par Dmitry G

Jul 19, 2018

Concise intro to much needed big data machine learning solutions

par Victor d O

Jan 09, 2019

I think we need in this module more pratical assignments.

par PRASHANT K R

Jun 07, 2018

very nice course it gives more insight to deep learning.

par Jair M

May 22, 2019

Some videos are missing, but anyway is a great course

par Amalka W

Dec 29, 2019

Course covers scalerble deep learning concepts

par Andrey O

Sep 07, 2018

Part with DeepLearning4J is not very good...

par Vinayak B

Jul 30, 2019

Really Helpful course for AI Enthusiasts

par Mobassir H

Apr 22, 2020

pytorch instructor was the best <3

par Valerio N

Mar 27, 2019

Very Complete course.

par Arati Y

Apr 09, 2018

It was nice

par Tobias H

Aug 26, 2018

n/a

par Pierre-Matthieu P

Nov 30, 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

Jun 07, 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.

par Jakob S

Mar 26, 2020

The course covers some very interesting and important concepts, however on a very low level. The reason for this might simply be the lack of time; one cannot properly cover methods for AI image processing, NLP, etc. in such limited space. I also had mixed feelings about the exercises: It is very nice to see applications of the tools discussed in the lectures, but unfortunately the exercises are so simple that they can be easily finished without really understanding the code.

par Manas S

Jun 19, 2020

The course instructors are very experienced and knowledgeable but the teaching part has not been done very well. The assignments were not up to the mark, and an attempt to included too many topics in a very concise format was made. Some topics like Feed-forward NN in Keras were covered very well but most other things were a disappointment.

par Jose L M G

Apr 01, 2019

Lo hago, el curso es muy bueno en cuanto al uso de la plataforma watson, pero falla en explicar los fundamentos principales con animaciones, ejemplo, el curso de pytorch de udacity enseña eso muy bien. En lo demas esta bien, pero al no contar con elementos visuales de ayuda en laclase de LSTM se hace tediosa.

par Jeet D

May 12, 2018

The course is very resource heavy, i.e. it has great intuitive resources, but the learning experience was very poor. Some of the instructors were very sparse with the material contents, some just brushed over the contents without much explanation and.

The quality of the course has to be improved.

par Julián M

Jun 16, 2020

You can learn several things from this course but you need to know Neural Networks and Deep Learning in advance. The content looks a bit disorganized but still pretty useful for day to day Deep learning implementations. Really cool the System ML integration with Keras.

par Daniel P

Jul 10, 2018

Too much focus on IBM platform, good overview on Keras/SystemML/DL4J though, some presentations could have been better prepared and implemented. Overall an average Coursera course and not a particularly great experience to work through the material.

par Eugene N

May 22, 2020

Something happened to the free 1CPU 4GB python environment on IBM watson studio. It is unavailable and so I had to struggle with Skills Network Labs instead. Please can this be checked?

par Robert F

Jul 04, 2020

Fairly okay course. Lectures were real hurried and high level. Had it not been for my Math and CS background I would not have gotten most of the material.

par Ceren A

May 10, 2020

Several lectures were superficial. I feel like I need to put a lot more time on my on to understand how to build a proper neural network model.