Zurück zu Neuronale Netzwerke und Deep Learning

4.9

Sterne

71,529 Bewertungen

•

13,740 Bewertungen

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.
In this course, you will learn the foundations of deep learning. When you finish this class, you will:
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network's architecture
This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
This is the first course of the Deep Learning Specialization....

Jan 18, 2020

Very structured approach to developing a neural network which I believe I can use as foundation for any project regardless its complexity. Thanks professor Andrew Ng and the team for their dedication.

Sep 02, 2019

I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)

Filtern nach:

von Michael C

•Sep 23, 2017

Excellent course. Surpasses Andrew Ng's original Machine Learning course in conceptual depth and ease of implementation. The lecture videos, quizzes, and programming assignments are all targeted towards someone who knows nothing about deep learning or machine learning, yet manages to elaborate on surprisingly advanced topics which you would not expect to make an appearance in an introductory course. It strikes a superb balance between simplicity and depth that is rare even in in-person university courses, and much rarer still in MOOCs. I will be taking all the rest of the courses in the Deep Learning Specialization. Well done.

von Hong X

•Oct 02, 2019

I've learned to build the basic binary classification model from conventional logistic regression to a shallow model (with one hidden layer) up to any layers of ANN. One of the most rewarding point for me is that I start using python (other than Matlab with which I have stuck for years until recently most cutting-edge open-source codes are found delivered in Python!). Although there is still a long way to go , I found well warmed up by those delicately designed step-by-step programming exercises in Jupyter notebook. Therefore, I do appreciate the course materials contributed by the lecturer as well as the exercises-designers!

von Chi W C

•Sep 13, 2017

Wonderful class. I started out not knowing anything about neural network or deep learning. I was able to follow the class lectures to get a sense of what was going on. The assignments were clearly structured and well organized, and serves as excellent examples in how to build this type of applications (by small building blocks and test each of the block carefully).

At the end, I was able to build my first neural network implementation in recognizing a cat!!

(However, I have uploaded 3 non-cat images, but NN failed by predicting these were cats. On the contrary, logical regression correctly predict the 3 images as non-cat).

von Carl G

•May 06, 2018

Andrew Ng is a thorough teacher and shows how online platform can be as engaging as taking a live class. His pace and style of writing slides is perfect for keeping pace taking notes by hand (my preferred way for efficient learning). He takes time to explain in depth how NN's work, and even more important his experience how to use them. Homework is a bit simple, but also appreciate to not be mired in coding details. Nice to be able to focus on how NN's works. Best part is that each piece of code can be fully tested against known output before used further. Illustrates nicely good practice once doing real coding project.

von LIM W X

•Jan 13, 2018

Through the Neural Networks and Deep Learning course, I have learned the fundamentals of neural networks and deep learning. The lectures are simple and easy to understand. The assessments have designed to test students in the fundamental knowledge of neural networks and deep learning. The assignments are designed to guide students on how to design and implement a shallow and deep neural networks, by applying what have been taught in the lecture. In conclusion, I enjoyed this course and I will definitely continue the deep learning specialisation courses to achieve my career goals. Thank you Prof. Andrew Ng and Coursera.

von Michael B

•Sep 18, 2017

Andrew, like no other instructor, manages to convey difficult material in a clear and concise manner. Even after many years experience in machine learning/deep learning, this course lead to many "aha" moments where many things I learned about the topic came together! The only criticism that I have for this excellent course is that I wish it would contain some, maybe optional, videos that go deeper into the math of for example backprop. I think this is a difficult concept to grasp and I imagine that if Andrew would sketch the proof with is clear and concise style, a lot more people had a much better understanding of it.

von David R T F

•Nov 01, 2017

Andrew does a fantastic job of making this material accessible. This course is a great introduction to deep learning and won't overwhelm you with the details of the underlying mathematics. If you understand some fairly basic linear algebra and know how to take derivatives you'll be fine. The lectures are incredibly clear, and this is one of the best Coursera classes I've taken. The only critique I have is that the homework could be a little bit more challenging - or (if that would undermine the introductory nature of the class) there could be additional optional problems that push students a little bit harder.

von HUA E C

•Oct 28, 2017

A review from a business student with some programming and statistic foundation.

The programming assignments are great, guiding you to build part by part of the model.

Whenever you feel unsure what to do, make sure you read the instruction carefully, as clues/hints are often in there.

It's feels so awesome that I could finally construct deep neural network by myself instead of using packages that I have "some kind of" idea what's happening behind the scene.

Thank you Andrew! Your courses really inspire me, and when I become a master some day I will share my knowledge and experiences to inspire younger generations!

von William L K

•Sep 06, 2017

Excellent course. Lectures are clear and concise. Professor Ng makes it seem so understandable despite the complexity of actual programming implementation! Assignments are both relatively straightforward (overall concepts) and tricky (keeping track of the matrix manipulations in Python). I don't know how many times I started a programming assignment, hit a wall in terms of programming errors, and came back to it after a time and getting through that error. Persistence, at least for me, was definitely a major component. Well worth the time put in. Looking forward to taking the next class in the sequence.

von Laith M A R

•Aug 18, 2018

I am so proud and confident of the things i learned. i never expected to learn this much from an online machine learning course, so many concepts that were vague to me in the past are now Crystal clear, and prof. Andrew does an outstanding explanation for each concept, not to mention that the programming assignments are extremely beneficial and cover every concept explained throughout the videos in a really cool, professional way. This has been only the first course in the deep learning specialization i am currently pursuing, and it made me so much more excited for the upcoming courses! thank you coursera :)

von Jong H S

•Sep 30, 2017

This course is really an essential first step to AI. Using Logistic Regression to kickstart is a great way to demystify Deep Neural Network. One of my greatest weaknesses in learning Deep Neural Network was keeping track of correct dimensions in matrices. This course has a special topic on that, very thoughtful indeed. Having taken Geoffrey Hinton's Neural Networks for Machine Learning, I still consider the programming assignments to be very challenging but there are plenty of materials that helped me getting through it. All in all, this is a timely, thoughtful and extremely effective Deep Learning course.

von Fezan R

•Apr 22, 2019

Andrew NG is the most humble and talented teacher I ever came across. This course is paced right for beginners like me, prior to this course I had taken his Machine Learning course. I had basic ideas of logistic regression and Neural Network before. But this course enhanced my learning and also Python is a big help. (though sometimes i have to look for documentation even for most simple things, like getting a random array of certain dimension, but it aint a big deal). The core of this course is the understanding of forward and backward propagation. Which Andrew did with great details and make it simplified.

von Mohammad H R

•Aug 30, 2019

Amazing course. Andrew has really streamlined the concepts, made the course easy to follow and at the same time leaves room for further analysis and curiosity. It is so well structured that can transfer complex concepts easily to you and therefore maintain the excitement in the student to keep on going at his/her own speed. What I loved most about the course was the fact that for some reason it seems like Andrew knows where to give you further explanation about what just happened or where you might get stuck in the code and in the lecture. Thank you Andrew. Such an amazing experience and great structure.

von Mallikarjun C

•Jan 31, 2019

I found this course to be extremely good. It covers nicely theory, implementation and application of Neural Networks and Deep Learning. Prof. Andrew Ng through his video lectures makes it fun and easy to learn this subject with the right emphasis on key points. The quiz's and program assignments are really good, reinforcing the concepts. In addition I found the Hero's of Deep learning conversation videos towards the end of each week, informative and thought provoking. This is my second course after taking Machine learning on Coursera. I am enjoying learning on Coursera. Thank you Prof Andrew and Team.

von Carsten W

•Dec 28, 2019

Fantastic course with well structured Jupyter notebooks for your Python programming assignments. The assignments were pretty easy due to extensive explanations and repetition of key formulas from the lectures within the notebook. To be fair to others, maybe it was also a bit easy, because I just recently completed Andrew's older Machine Learning course (with programming in Octave and still highly recommended for a slightly deeper foundation in ML - I think), so I was already well familiar with the key concepts, vectorization etc, which I only had to transfer to Python. In any case, awesome course!

von Heshmat S

•Dec 27, 2017

I've taken Andrew's "machine learning" course before, which I loved so much and learned a lot from it. The only issue with it was the use of "matlab/octave"; fortunately, he switched to "python" in this specialization course. :-)

This first course in the "deep learning specialization" is a very well though-out introduction to deep learning. Starting from logistic regression, Andrew builds upon the materials and masterfully introduces the more sophisticated concepts one after another. The programming assignments make the course even more fun and practical. Loved the course.

Thank you Andrew & Co. :-)

von Obaid S

•Jul 06, 2019

This course is one of the best online course I have taken so far. With basic math knowledge (you just need to know what is a vector and what is a slope) you can complete all the assignments and the course itself. In this course, you get in-depth knowledge of how a neural network works by implementing it yourself. The best thing about this approach is that you will be very confident as you start playing with high-level libraries like tensorflow, since you will know what is going on under the hood. I think this course is a great place to start if you are new to deeplearning before using any library.

von Fabian A

•Oct 28, 2017

I really enjoyed the Jupyter Notebook approach as it really suits my experience with Python3 and love of pedagogical and sound presentation of theory. The code can sometimes be a bit too forgiving in that it would be possible to go through it without thorough examinations of dimensions, calculations and the like. I, however, am doing this for learning rather than certification so it was a minor issue.

Really nice videos, a clear structure and a very thoughtful balance between the complexities of math and the "get things done" possibilities that jupyter notebook and Coursera permits. A great course!

von critics

•May 14, 2018

This course is friendly to novice because Andrew is adept at making the originally complicated lessons easy to inteprete, and his clear pronounciation and moderate speed help students catch up his pace without extra effort even for non-native English Speaker.More importantly, we all known that Andrew is known as a prominent AI scholar around the world, and his intelligence is sparkling through the course, for example, the systematic course structure reveals his in-depth knowledge, as well as the practical advices on buliding a deep learning model shows his rich experience in actual implemention.

von Sikang B

•Dec 04, 2017

Compare to the machine learning class years ago, this revamped NN and DL class took very modern approach and really take machine learning education to the next level by using new technologies, better programming models and last but not least, Python Notebook for education.

Assignments are helpfully guided, however the guidance felt a bit too excessive at times. Some text could be better delivered as hints rather than instructions.

This course is less demanding and is definitely perfect as an introduction course. The interviews are super relevant and highly engaging. Make sure you don't miss them.

von José A

•Sep 15, 2017

It's only my first week in the course, and I'd say it's been good. It can be a little bit tedious to catch up with the terminology if you haven't seen any Data Mining or Machine Learning . Nothing that a good devotion of Google and YouTube-fu can't tackle.

Other than that, I have a very basic knowledge in the topic and I have had to do some good research about it. The 2nd week's lectures goes through each of the steps in building a Neural Network, including the explanation of a Gradient Descent, Logistic Regression, and derivatives.

I'll see if I can update the review after finishing the course.

von Paulo A F

•Aug 26, 2017

Andrew Ng is the best! Congratulations to all the team involved in the course. It i at a very good level for everybody to join in. As an experienced programmer I though the programming assignments were on the easy side, but I guess they are at the right level for people coming from other areas. As for the maths, I think is a good idea to leave the deep stuff out of it and get people building the NN as a long as the maths behind it is solid, which it is in this course. People can delve deeper in other sources. I'm quite excited for the next 4 courses! All the best to the team and to the students!

von Mark D

•Mar 22, 2018

I loved the programming assignments. The tasks are nice, the visualization of the neural networks' decision boundaries is very helpful, and the setup with the Jupyter notebooks is just awesome. In other courses it is often required to set up a programming environment first, which sometimes takes more time than the programming exercises themselves. In this course it was possible to dive into Python and Numpy immediately - without worrying about file paths, environment variables, compatibility issues and other nuisances. The lectures were also very good. All the concepts were explained very well.

von Rahul K

•Feb 27, 2018

Beautifully structured course! Feels like a walk in the park if you've already completed the 'Holy Bible of ML', i.e., Andrew Ng's Machine Learning course on Coursera.

Very good programming guidelines, and a gentle introduction to anyone who isn't aware of the core concepts on Machine learning.

If you're wondering whether you should complete the Machine Learning course first, by all means, go ahead. However, I can guarantee that there will be no hardships faced even if you're a beginner in ML and want to dive head-first into Deep Learning.

After all, it's Andrew Ng who we're talking about here! :)

von Omair M

•Nov 25, 2017

Prof. Andrew Ng explains all concepts from a very fundamental level and even nervous students will feel encouraged by his insistence on "don't worry about it" for derivations you don't understand. The assignments have a lot of hand-holding but I needed that to focus on other more important concepts instead of debugging python code which can be learned in a different course. Overall, I have learned how to build a deep neural network using a building-block approach and gained confidence regarding this domain which I had previously taken to be mysterious and cryptic and perhaps for the elite only.

- KI für alle
- Vorstellung von TensorFlow
- Neuronale Netzwerke und Deep Learning
- Algorithmen, Teil 1
- Algorithmen, Teil 2
- Maschinelles Lernen
- Maschinelles Lernen mit Python
- Maschinelles Lernen mittels Sas Viya
- R-Programmierung
- Einführung in die Programmierung mit Matlab
- Datenanalyse mit Python
- AWS-Grundlagen: Mit der Cloud vertraut werden
- Grundlagen der Google Cloud-Plattform
- Engineering für Site-Funktionssicherheit
- Englisch im Berufsleben
- Die Wissenschaft des Wohlbefindens
- Lernen lernen
- Finanzmärkte
- Hypothesenüberprüfung im öffentlichen Gesundheitswesen
- Grundlagen für Führungsstärke im Alltag

- Deep Learning
- Python für alle
- Data Science
- Angewandte Datenwissenschaft mit Python
- Geschäftsgründungen
- Architektur mit der Google Cloud-Plattform
- Datenengineering in der Google Cloud-Plattform
- Von Excel bis MySQL
- Erweiterte maschinelles Lernen
- Mathematik für maschinelles Lernen
- Selbstfahrende Autos
- Blockchain-Revolution für das Unternehmen
- Unternehmensanalytik
- Excel-Kenntnisse für Beruf
- Digitales Marketing
- Statistische Analyse mit R im öffentlichen Gesundheitswesen
- Grundlagen der Immunologie
- Anatomie
- Innovationsmanagement und Design Thinking
- Grundlagen positiver Psychologie