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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....

Apr 07, 2019

A bit easy (python wise) but maybe that's just a reflection of personal experience / practice. The contest is easy to digest (week to week) and the intuitions are well thought of in their explanation.

Aug 27, 2017

This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.

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von WALEED E

•Dec 17, 2018

This course formed a concrete background in building multi-layers neural network from scratch. The best advantage of this course is I was able to immediately apply the knowledge I gained into real world problem like humanoid navigation towards known targets. Illustration is great in terms of mathematical explanation and coding in a step by step walk through.

von Abdessalem H

•Dec 03, 2017

This is one of the courses I enjoyed the most. For someone who has little to no knowledge in calculus and programming, I found the course is well tailored for all kinds of background. The pace is not so fast and Andrew is making it so easy even for beginners to grasp the new jargon and formulae. Thank you Coursera. Thank you Andrew.

von KOTHAPALLI V A S S

•Jun 19, 2019

The course gives you very deep intuitions about neural networks and glimpse of deep learning .NO special mathematics course is not required formal understanding of high school calculus is enough .The programming assignment are too good actually they multiply your understanding, you get a feeling of real world application .

von Sreenivas N M

•Dec 17, 2019

Excellent course to start learning about the basics of deep learning. Not just a simple copy paste cat vs dog classification course. But rather, a proper mathematical understanding of logistic regression, how it can be used as a single layer network to building one hidden layer network to multi layer hidden neural networks.

von Nikhil S

•Jan 16, 2020

Neural Networks and deep learning is absolutely a great course for beginners. Those who have interest in this field can go for this course. It will clear all your doubts and you will enjoy this course. It was absolutely helpful for me . It helped me in gaining new skills and expand my knowledge.

von mostafa n

•Mar 04, 2020

This course really helped me and gave me new skills by applying my first neural network in very cleared way from prof Andrew ng as usual. big thanks for everyone who worked on this course and helped us to increment our knowledge, i recommend this course to everyone.

von SAGAR B

•Sep 10, 2017

A great course to understand basic concepts of Deep Learning. If you are a beginner in Deep Learning and thinking if you should invest your time and money here, don't give a second thought and join right away. Andrew Ng never disappoints!

von Mihai C

•Jul 15, 2019

Very well structured, the code is much better than in the Machine Learning course that was initially posted on Coursera, and the use of Python instead of Matlab makes things much easier and friendly for everyone. I really enjoyed it.

von Nguyen H T

•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.

von Zillur R

•Jan 04, 2020

At first, I want to thank the course teacher and all the others for providing us such a wonderful course. The way the professor teaches is really very very helpful. Thank you all again and keep it up.

von Anjan D

•Oct 01, 2017

Excellent course with great assignments. I have learnt from the beginner level in DL. It also helps one to brush up the calculus and linear algebra knowledge very much.

von Kieran S

•Oct 22, 2017

Extremely well structured course that gives you good intuition about how deep learning works by starting with simply examples and adding layers of complexity.

von James G

•Jan 09, 2019

Great content and pace was more than manageable.

(Unrelated but worth mentioning is that I have found Coursera the platform to be incredibly buggy)

von Gurudutt N

•Nov 29, 2019

Such a complex subject made look like so simple. Every concept is covered in detail. Thank you Andrew Ng.

von Benito C

•Sep 02, 2017

Very hard work in designing the notebooks so the pupils's learning processing is maximized.

von Michelle

•Dec 20, 2019

very clearly explained and can't find anything better, loved the intuition part the most.

von Aman K S

•Jul 10, 2019

The most comprehensive and illustrative Machine learning course I could get through.

von Suddhaswatta M

•Apr 26, 2019

Converting Mathematical equation to Python code are very well explained !!!

von Lakshya K

•Dec 20, 2019

Lovely course and it will surely boost my career. Everyone should do this.

von Md. S R

•Dec 20, 2019

An excellent course to start your journey on A.I. and Deep Learning.

von Anastasiya L

•Jan 28, 2019

Easy to follow class, breaks everything down to small simple steps.

von Chinmay H

•Dec 20, 2019

Andrew Ng is an awesome instructor!

von 华德禹

•Aug 23, 2017

greate

von Stephen K

•Nov 07, 2019

Tying your shoelaces is easy...if you have two hands. Some reviewers say this course is easy too. But you will be confronted with multiplying matrices and some differentiation. More than anything, I found it difficult to keep track of the different matrices, and particularly their dimensions, which if you do this course you will see is vital. There's also a lot of notation to overcome. You will need to understand some python, particularly how to extract values from tuples or dictionaries, and being familiar with user-defined functions will also help. So, easy?

The course starts with a 0-level neural network and builds up to a deep neural network. It's a nice way to easy yourself into what is clearly a complicated subject. The downside (at least for me) was that each week I was hit by yet more new notation, and I felt that some of what I'd been taught in the previous week (and was clinging on to by my fingertips) was almost redundant. It made my head spin. Nonetheless, I persevered and passed the course.

So, I've gained an appreciation of approximately how a neural network works. I could not build a neural network from scratch without massive recourse to my notes and assignments, and plenty of time. Is this how people build neural networks, or are they using libraries to make the job much easier (Tensorflow, Keras, etc.?) Or, can I use the final assignment as a template and apply this to many problems? I don't know.

I thought the notes were quite poor. There is a mountain of writing on most slides at the end. I scribbled more notes to explain Andrew's notes, otherwise a week later it'll be clear as Aramaic. However, Andrew repeats and explains well what's happening. He has a calm and reassuring manner, which I really liked.

People have complained about assignments being too easy. Not for me. I thought they were a good way to reinforce the lectures, and provided a means to see how a neural network could be built in practice. The assignments are more like lectures with your participation than traditional assignments. This is a plus point, in my view.

Finally, I'm still blown away how just a 'simple' logistic regression with sigmoid activation function can predict cats from random images so well. I've done the course, but it's like magic!

von David R

•Oct 01, 2019

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

- KI für alle
- Vorstellung von TensorFlow
- Neuronale Netzwerke und Deep Learning
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