If you want to break into AI, this Specialization will help you do so.
Tensorflow Convolutional Neural Network Artificial Neural Network Deep Learning Backpropagation Python Programming Hyperparameter Hyperparameter Optimization Machine Learning Inductive Transfer Multi-Task Learning Facial Recognition System
Accessible for free. Completion certificates are offered.
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CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
and 2 more instructors
This specialization includes these 5 courses.
Reddit Posts and Comments
15 posts • 248 mentions • top 101 shown below
16 points • zawerf
This seems to be on coursera so is it the same as https://www.coursera.org/specializations/deep-learning by Andrew Ng? Just wondering if they make it any more rigorous for stanford students.
15 points • DeepLearningSurfer
Motivated to learn DL but already in a very good engineering school that's completely unrelated
I'm currently studying in a really good engineering school in France and I'll graduate in 3 years. Basically I'm very interested in Machine Learning and Deep Learning and my math background is quite good: linear algebra & calculus are no problem, have to learn some statistics though. I've been lurking around here and a few other subreddits the past week and decided to take Deep Learning Specialization by Andrew Ng. I think DL is a very powerful tool that'd be very useful to me even though my engineering degree isn't very related to it. I'm very motivated to do this right now but if it isn't somehow useful to me i might end up in a situation where I've wasted a lot of time. In three years I'm already guaranteed to get a good job but ML and DL just seem so fun to me, using a huge amount of data with statistics, linear algebra and calculus to create AIs... What do the more experienced people here think?
44 points • rushjustice
Just Finished Coursera's ML Class | Next Steps
As per one user's great advice from a post about two weeks ago, I began my journey into ML and data science. I completed Andrew Ng's course on ML and found it extremely interesting. I loved every bit of it. I was on coursera every day, and completed everything in that course. It was very cool to go on Kaggle, read some tutorial kernals, and just find myself noting what the provider should have done differently as per Prof. Ng's advice. I feel like I have a solid understanding of the fundamentals of some of the most basic and widely used ML algorithms today, and how to use them properly.
I'd now like to contribute on Kaggle, but I really do not have the skills to do ML (or really any data science) in Python/R. Though I probably could mash up some code from some popular kernals, I really wouldn't know what I was doing, and so that would be pointless. I've discovered two courses (specializations) that focus on deep learning / general data science using Python, that seem pretty good. At this point, I'd like to learn Python over R.
Has anyone taken these courses? Does anyone have an opinion on what are some good ways to learn Python with data science? Sometimes I think I could be overcomplicating this, but I really don't think it's wise to jump into Kaggle, only to possibly burn myself out because I don't know Python. Perhaps someone has been in a similar situation and can help guide me? Again, I could just jump into the above two courses, but if anyone can help optimize my solution so that I start in a better direction, that would be huge!
Thanks you, everyone! As it stands, my game plan is to get on Kaggle, build up a portfolio, and use that to help me land a job in the ML realm. I've actually found some interesting jobs that combine both my collegiate background with ML. Pretty neat.
12 points • node0
His original course was great, but some of the material is now dated. Fortunately, he has several new courses available as part of the Deep Learning Specialization. You can still access them for free by auditing each course.
8 points • Silver5005
Check out Andrew Ng's deep learning course on Coursera. It is highly praised in this industry as one of the best beginner tutorials and you can try it for free.
Pro tip: sign up for free week trial on Coursera, finish at least one chapter/module of the course and you can access the material for the entire course even after trial period ends. Or you can pay for it if you aren't cheap like me.
6 points • inkplay_
https://www.coursera.org/specializations/deep-learning This is the second part, the first part is just machine learning, I believe FastAi has GANs and this doesn't.
6 points • ricewar
Sure! the feature vectors have value for each hero, where -1 indicates dire, 1 radiant, and zero being absent. the label or 0 or 1, which represents radiant win.
I tried different topologies, but in the end I used 5 hidden layers with RELU activation functions. The final activation is a sigmoid function.
If you're interested in learning more, I can't recommend enough Andrew Ng's courses on coursera.
13 points • data_science_is_cool
Your Thoughts on Coursera's Deep Learning Specialization with Andrew Ng?
I would really like to know if anyone found this specialization valuable and worthwhile? I have taken some courses on Coursera that were not always great, just wanting to get feedback before making this investment of my time.
3 points • Klarthy
ML.NET isn't a mature ML platform yet and is missing native support for a lot of ML applications. In my opinion, you'll have a much higher difficulty getting started because the ML community is heavily organized around Python. Not only will you be missing out on better frameworks, books, documentation, and how-to blog articles, but also things like Jupyter notebooks.
While others have recommended Andrew Ng's Machine Learning course, I would recommend his Deep Learning specialization instead. There is a mix of programming a variety of neural networks from scratch in Python as well as using Tensorflow. It is a much longer effort than the ML course, but concepts like linear regression and support vector machines (only covered in the ML course) have limited applications. Coursera recently released new Tensorflow-based courses, but I haven't tried them.
3 points • healydorf
Andrew Ng's deep learning course covers the fundamentals pretty well IMO:
That's a good way to at least get your feet wet. Most of the jobs under AI/ML require masters/doctorates though, so if you're not currently considering grad school I would strongly recommend it.
3 points • blanket13
Who am I to give any recommendations. I myself, am learning to be a better DS. Having said I can least talk about my experience. Most of the learning for me has been through online learning (courses, blogs and papers) and small small projects. I think this journey was like assembling puzzle pieces. Filling void in my knowledge through reading and talking to people.
I want to mention this though. The biggest improvement in my knowledge was going over this course: https://www.coursera.org/specializations/deep-learning and doing the assignments. My industry experience helped me master these concepts
3 points • gualterio7878
The new Andrew Ng deep learning course is this? --> https://www.coursera.org/specializations/deep-learning ??
3 points • ZhuNorman
I am learning Andrew Ng's Deep Learning Specialization on Coursera. https://www.coursera.org/specializations/deep-learning It is really helpful, and includes a lot details. Maybe, you can check it.
2 points • the_empty
Thanks for your opinion, appreciate it. Here's the course for those interested: https://www.coursera.org/specializations/deep-learning
2 points • healydorf
I would highly recommend the Deep Learning course on Coursera. I believe it is free to audit.
> 1) Is the only way to get a job in this field to get a Masters as it seems?
Absolutely not, but if this is a field you're dead-set on, I would suggest grad school because it opens an awful lot more doors than an undergrad program.
> 2) How would I find companies or teams doing this type of work?
All the BigN's are doing AI/ML stuff. Probably plenty of big banks as well. There's also lots of orgs not currently doing ML things that could be doing ML things if you are open to being an evangelist, but you'd probably need to put a few years in to have that sort of clout.
2 points • Vikhyat333
I'd recommend Deeplearning.ai's Specialization on Coursera, it has great explanations and hands on assignments.
2 points • Siref
I got billed twice for Deep Learning Specialization - Sequence Models.
As some of you are aware, the Deep Learning Specialization's sequence model has been pushed to late January.
I'm enrolled in the specialization, and I got billed on the month of December for the Sequence Model, which never started. Now, a couple of hours ago I got billed again. I can't see Coursera's Help Center in their site. And the FAQ isn't helpful. In addition, if I go to My Purchases there's no way for me to ask for a Refund, since all of them "Refund deadline has passed"
How should I proceed in this case?
14 points • goldmyu
Finished with coursera ML course, whats next?
I have just finished with the ML course on coursera by Prof Ng, was thinking about the follow-up series also available at coursera by Prof Ng for deep - learning specialization: https://www.coursera.org/specializations/deep-learning
I have also come across this free google course at udacity: https://www.udacity.com/course/deep-learning--ud730
and these nano-degrees as well at udicaty:
Machine Learning Engineer Nanodegree https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009
Artificial Intelligence Engineer https://www.udacity.com/ai
DEEP LEARNING NANODEGREE https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101
Did someone here had any experience with these ? are there other better courses\speicalzation that you recommend of?
7 points • ajaysub110
I suggest that you move onto deep learning after that because that's where you actually start learning concepts applicable to ongoing research and industry. I recommend picking one of the following:
- Andrew Ng's Deep Learning Specialization: After I finished his ML course I took 4/ 5 courses from the specialization. A bottom up approach (Teaching the concepts first and then building those ideas into code). Slightly more practical-oriented too as compared to the ML course. The reason I stopped after course 4 was I couldn't help but feel overwhelmed by the amount of concepts being taught in between programming assignments. So I decided to take a break, do a more practical course and then get back.
- Jeremy Howard's Fast AI course: Consists of 7 long video lectures (\~2hrs each). No Programming assignments. Great insights on Kaggle competitions. Taught using his own Fast.ai library that's derived from pytorch. This course has great reviews, but I didn't really like the teaching approach which is completely opposite to Andrew Ng's. He starts with the code, spends 3 videos on that and then starts slowly trickling down to the concepts.
- Deep Learning with Python (Book) by Francois Chollet (Creator of the Keras framework and Google Brain scientist) - The best practical deep learning resource of these, in my opinion. Plus, its in Keras, the most widely used high level DL framework right now and hence really useful to learn. Lots of code examples. Another thing I really like is that the 'programming assignments' are introduced right along with the theory making the concepts more comprehensible.
4 points • davidshen84
4 points • A01u
Try the deeplearning ai course specialization by Andrew Ng if your interested in learning about Neural Networks from the ground up. Fastai works as well but it is more of a top down approach to learning.
1 points • internetdigitalentre
Launch your career in Artificial Intelligence with a Deep Learning Specialization
1 points • [deleted]
Coursera deep learning course by Andrew Ng
1 points • Lt-Skeleton-SFW
Andrew's courses are pretty good, and the [Deep Learning Specialization] (https://www.coursera.org/specializations/deep-learning) cource is a good continuation.
1 points • BigTheory88
Here you go my friend! https://www.coursera.org/specializations/deep-learning
1 points • monkeyunited
I was doing really well as a data/report analyst. The work was relaxing, never short deadline, work-from-home, and I was recognized by 2-3 level higher.
Eventually data side of work became easy and routine. I noticed myself spending more time fighting with stingy "data stewards" to get data; if someone else made a similar report, I had to waste time figuring out why our numbers were different, ...etc. My work became more and more political.
I just don't see myself staying in BI world any longer. If I were to get a management role, it'll just mean more political work and the data side isn't going to be more interesting.
Your worry is rational. I just want to point out that often time simple algorithms (such as logistic regression) are sufficient and impactful. You should also take Andrew Ng's deeplearning.ai to see how easy it is to get some well-performing neural networks up and running.
1 points • gerry_mandering_50
Convex and order(1): linear programming
Non-convex and order(?): deep neural networkss
Case studies for optimal combinations? Not that I know. Why not find the optimal combinations yourself? The Dense layer is a good combinatorial weight optimizer. really. https://www.coursera.org/specializations/deep-learning
1 points • simoneobo
1 points • redditonlyforu
1 points • erkalsedat
I am currently enrolled in the course, and I wish I would take the course before (I have a master of science degree in petroleum engineering degree). Andrew Ng is one of the best instructors in the AI area. By taking the course you will have some Matlab coding skills and get intuitions of machine learning as well as deep learning. So I highly recommend it. (Plus, you can take the Deep Learning Specialization after finishing this course.)
1 points • Antonioe89
Have a look at this online course by Andre Ng: https://www.coursera.org/specializations/deep-learning
Includes videos, homeworks, quizzes, etc.
1 points • aolchawa
Best what I can recommend is to start with a deep learning specialization over at Coursera https://www.coursera.org/specializations/deep-learning No need to do it all, but first 1-2 courses are very good to start if you want to understand how go approach development of your own neural network model. Then they teach about the frameworks and in general how go structure your projects. Very interesting.
1 points • aolchawa
Basic math really, some functions, derivatives, operations on matrices. Best to start with deep learning specialization over at Coursera: https://www.coursera.org/specializations/deep-learning
1 points • Jedibrad
I took a graduate level class in it my senior year of undergrad, and my job paid for me to do the Coursera Deep Learning Specialization. I just finished that yesterday, actually.
RNNs aren't my specialty, I'm more of an image processing guy. The Convolutional Neural Network course was very good, I'd highly recommend it. Coursera is the way to go to learn stuff like this, in my opinion!
1 points • white_noise212
Thank you for sharing OP. I'm looking forward to perfectionning my skills in these fields and start sharing the knowledge i've learnt with the community. For me, I'm currently enrolled in Andrew Ng's Deep learning specialization on coursera, and it's a really good starting point. He's tackling the theoritical as well as the practical aspects of the different algorithms, which I've found really interesting. Other good point is that he's using Python/Tensorflow for programming assignements. It's a really good material for beginners, has to be definitely checked out. Good luck everyone in your learning journey, and don't hesitate to spread the knowledge by sharing it !!
1 points • Isonium
Are you wanting to code the neural network completely in Python from the ground up or are you looking to use something like TensorFlow in Python?
The first two courses in this Deep Learning Coursera specialization walks you through building a numpy based vectorized deep neural network. The first course can be taken without paying and all the notebooks are available to you. You build a fully functional deep network that recognizes cats. The 2nd course is about regularization and you can still watch all the videos and it goes over types of regularization, normalization, and hyper parameters. However to access the notebooks where you implement them requires paying for the course. At the end of the course 2 it walks you through TensorFlow in the final notebook.
I have taken the first two to understand the internals of neural networks. I am just starting the 3rd course. You don’t need to know a ton of Python to take this class. And the math is not terribly hard to follow. Calculus helps.
1 points • its_all_relative_
Do this: https://www.coursera.org/specializations/deep-learning
1 points • Mas0n8or
I strongly recommend these (free) courses by Andrew Ng, a brilliant computer scientist and a pioneer of machine learning.
1 points • healydorf
> How can I get started?
Get. Good. Grades. It makes getting into grad school generally easier.
+1 for Andrew Ng's Deep Learning course. My team went through it to get the engineers/analysts up to speed on some of the fundamentals. Pretty sure you can audit it for free, but you won't have access to the coding exercises.
Around the start of your junior/senior year, start talking to professors as well as your program adviser/counselor about grad school. Most of the current work around ML/AI is being done by researchers supported by seasoned engineers/analysts. Rarely are fresh undergraduates assigned to do the supporting or leading work for such projects.
1 points • konddmy
The old Octave version was about ML in general (touching Multi-Layered perceptrons as well), the Python version is about DeepLearning. However, it covers “shallow” NN as well, it just has much more “state-of-the-art” DL-related topics as those became kinda “default” solution in the industry in recent years. So, the basic algorithms (like gradient descent, BP) are pretty much same (but presented a little differently) - just more accent on DL-specific pipelines.
You can read more in the description, one more difference is that DeepLearning is a whole specialization on Coursera (https://www.coursera.org/specializations/deep-learning) - not just a single course. Also, it’s quite expensive, but actually Andrew (automatically?) checks Jupyter Notebook exercises (submitted through Jupyter) even though “submit” button is unavailable in review mode :) - at least in January 2018.
P.S. I believe the main reason for choosing Python was that TensorFlow (which is using NumPy) is one of the most popular frameworks for DL. And probably convenience of Jupyter (people can write code online without installing Python + guidances and checks can be mixed with actual code) as well.
1 points • konddmy
Actually it’s quite googlable: https://www.coursera.org/specializations/deep-learning.
1 points • Gobi_The_Mansoe
Coursera also has a more recent deep learning specialization that is taught by the same guy (Andrew Ng). I'm taking it now and it is pretty awesome. Taught in python using jupyter notebooks.
1 points • 16bithustler
If you're interested in the Deep Learning trend, Coursera offers a specialization where you can opt to pay 50USD/month or audit the courses for free. The content is quite good.
1 points • DidiBear
The best ressource that teach me DeepLearning is the Andrew Ng courses on Coursera : https://www.coursera.org/specializations/deep-learning (It's totally free : subscribe as free listener)
1 points • ricewar
I really enjoyed Andrew Ng's courses on Coursera.