Deep Learning
Below are the top discussions from Reddit that mention this online Coursera specialization from DeepLearning.AI.
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 MultiTask Learning Facial Recognition System
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Taught by
Andrew Ng
Instructor
and 2 more instructors
Offered by
DeepLearning.AI
This specialization includes these 5 courses.
If you want to break into cuttingedge AI, this course will help you do so.
Andrew Ng
28 mentions
Hyperparameter tuning, Regularization and Optimization
This course will teach you the "magic" of getting deep learning to work well.
Andrew Ng
4 mentions
You will learn how to build a successful machine learning project.
Andrew Ng
5 mentions
This course will teach you how to build convolutional neural networks and apply it to image data.
Andrew Ng
14 mentions
This course will teach you how to build models for natural language, audio, and other sequence data.
Andrew Ng
8 mentions
Reddit Posts and Comments
3 posts • 235 mentions • top 50 shown below
16 points • zawerf
This seems to be on coursera so is it the same as https://www.coursera.org/specializations/deeplearning 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
Hey all,
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.
https://www.coursera.org/specializations/deeplearning
6 points • inkplay_
https://www.coursera.org/specializations/deeplearning This is the second part, the first part is just machine learning, I believe FastAi has GANs and this doesn't.
13 points • data_science_is_cool
Your Thoughts on Coursera's Deep Learning Specialization with Andrew Ng?
https://www.coursera.org/specializations/deeplearning
​
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.
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.
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/deeplearning and doing the assignments. My industry experience helped me master these concepts
3 points • healydorf
Andrew Ng's deep learning course covers the fundamentals pretty well IMO:
https://www.coursera.org/specializations/deeplearning
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 • 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 howto 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 Tensorflowbased courses, but I haven't tried them.
3 points • gualterio7878
The new Andrew Ng deep learning course is this? > https://www.coursera.org/specializations/deeplearning ??
3 points • ZhuNorman
I am learning Andrew Ng's Deep Learning Specialization on Coursera. https://www.coursera.org/specializations/deeplearning It is really helpful, and includes a lot details. Maybe, you can check it.
2 points • Reading102
No worries.
Hyperparameters are basically parameters of a model that you would tweak, that would affect how it learns from data.
For example, in Neural Networks, hyperparameter tuning would be things like how many layers your model has, how many neurons/hidden units does each layer have, which activation function you use, etc.
If you are purely interested in Neural Networks, I highly recommend this specialization. It teaches you a lot about the intricacies of using them.
https://www.coursera.org/specializations/deeplearning?utm_source=deeplearningai&utm_medium=institutions&utm_campaign=WebsiteCoursesDLSTopButton
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.
https://www.coursera.org/specializations/deeplearning
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?
Thanks!
2 points • the_empty
Thanks for your opinion, appreciate it. Here's the course for those interested: https://www.coursera.org/specializations/deeplearning
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 followup series also available at coursera by Prof Ng for deep  learning specialization: https://www.coursera.org/specializations/deeplearning
I have also come across this free google course at udacity: https://www.udacity.com/course/deeplearningud730
and these nanodegrees as well at udicaty:
Machine Learning Engineer Nanodegree https://www.udacity.com/course/machinelearningengineernanodegreend009
Artificial Intelligence Engineer https://www.udacity.com/ai
DEEP LEARNING NANODEGREE https://www.udacity.com/course/deeplearningnanodegreefoundationnd101
Did someone here had any experience with these ? are there other better courses\speicalzation that you recommend of?
Thanks(:
2 points • Vikhyat333
I'd recommend Deeplearning.ai's Specialization on Coursera, it has great explanations and hands on assignments.
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 practicaloriented 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 • 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.
Deeplearning.ai https://www.coursera.org/specializations/deeplearning
Fastai https://course.fast.ai/
4 points • davidshen84
https://www.coursera.org/specializations/deeplearning https://www.coursera.org/specializations/tensorflowinpractice
7 points • lifeadvicesponge
I can refer you to some MOOCs for deep learning:

Check out Stanford's CS231n course . It's specifically about applications of deep learning to computer vision. Have a look at both the Spring 2016 and Winter 2017 iterations.

Stanford's CS224n course covers Deep Learning applied to natural language processing.

Apart from that you could refer to Andrew Ng's Deep Learning specialisation on Coursera. The fourth course covers CNNs (including things like YOLO and Siamese Networks which aren't covered in CS231n; at least the spring 2016 version). The fifth course covers sequence models (RNNs, LSTM, GRU)

You can also look at Coursera's Intro To Deep Learning by the Russian University HSE. While the quality of lectures is a hit and miss their programming assignments are more comprehensive than the Andrew Ng courses. It also covers autoencoders which aren't covered in any other course mentioned above.
1 points • rtayek
ng has a few courses on coursera. consider this specialization: https://www.coursera.org/specializations/deeplearning. the math specialization would be good also.
3 points • bandalorian
Any advice for a data analyst looking to cross over to deep learning/AI?
I did my bachelors in mathematical statistics. I've been working in analytcs/data analysis for a number of years so am comfortable with relevant programming (SQL, python, R). I've also taken a number of classes and certifications in data science/big data/ML/AI  though in terms of ML I've had limited experience using this in practice (just done a bunch of random projects for different courses). I have familiarity with fundamental ML concepts but that's about it.
I'm currently doing the coursera deep learning specialization, and I'm now starting to think about how I can build a path to actually getting working with it at some point. Udacity offers a 9 month self driving car engineer program which seems timely with everything that's going on, and also a direct practical application of everything. I also find computer vision extremely interesting, so autonomous driving seems like a good setting for me.
What other things could I be doing? I have time, and I find it interesting so I'm just going to chip away until either I qualify for something or the field has grown enough that they'll take me out of desparation :) Trying to methodically add to my educational background with the goal of being able to go to an interview and have a shot at getting hired. Any advice is appreciated!
Edit:Funny side note  realized a couple of years ago I asked what a good entry level job was for someone looking to transition into the ML field (but lacking full qualifications) and the the response was pretty much start as a data analyst. So that's what I did, and it worked out well so far. Hoping this sub can keep guiding me in the right direction lol
1 points • Righteous_Dude
I am currently enrolled in that course, taught by Andrew Ng of Stanford, and I also recommend it. I'm not very far through it yet.
Once I complete that, I plan to go through this 'deep learning' specialization, a set of five courses.
1 points • NFLAddict
It's aimed at beginner's/ or better phrased: people new to the field of ML to give an overview of what ML is. keep in mind that ML is alot more than just programming. there's a ton of math involved (requires understanding of various math topics). but this course is not taught in python, and might not be exactly what you're looking for given your comment.
perhaps you'll like this more: Deep Learning Specialization
Also quite popular (by the same teacher actually), its a 5course series, in python. you can obviously read about them more in the descriptions etc.
I should add, if you're not aware: you can take any class on coursera for free. many people mistakenly believe they have to pay for any class.
paying for a class gives you a certificate that shows you completed it (if that's of any value to you, then you can do that), but if you're more so just trying to learn the material and could care less about the certification;
for any class: when you click the 'enroll for free' button: a popup will show how its a 7day freetrial then some amount; but at the bottom there is an option to audit the course
which you can select; which allows you take anycourse you want for free
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 • simoneobo
Questo https://www.coursera.org/specializations/deeplearning
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 • Sideralis_
Ho visto che ti interessi di data science. La specializzazione in Deep Learning è fatta molto bene
1 points • aolchawa
Best what I can recommend is to start with a deep learning specialization over at Coursera https://www.coursera.org/specializations/deeplearning No need to do it all, but first 12 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 • 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 • BigTheory88
Here you go my friend! https://www.coursera.org/specializations/deeplearning
1 points • monkeyunited
I was doing really well as a data/report analyst. The work was relaxing, never short deadline, workfromhome, and I was recognized by 23 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 wellperforming neural networks up and running.
1 points • redditonlyforu
https://www.coursera.org/specializations/deeplearning
3 points • QuantMountain
Yes, you can see the connection weights and the output of each neuron all the way up to the final forecast.
Also, the left panel holds the controls for “hyperparameters,” which are the choices one makes for how a neural network functions.
Anyone who wants to learn more over the internet can check out Andrew Ng’s Deep Learning class at Coursera (https://www.coursera.org/specializations/deeplearning) or the fast.ai series (http://www.fast.ai/). I think the fast.ai course is totally free to follow along.
1 points • grudev
I finished (and enjoyed) that course, so I then started Andrew Ng's Deep Learning Specialization, which is divided into 5 different courses (I have yet t finish them all):
 Neural Networks and Deep Learning
 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
 Structuring Machine Learning Projects
 Convolutional Neural Networks
 Sequence Models
I've finished 1 and 2 and found that they reinforced the concepts you learned in the ML course but were much more "hands on" and understandable... Using Jupyter notebooks instead of Octave didn't hurt either :)
I'm planning to start my own project and do course #3 concurrently so I don't start forgetting what I've learned.
1 points • Mas0n8or
I strongly recommend these (free) courses by Andrew Ng, a brilliant computer scientist and a pioneer of machine learning.
https://www.coursera.org/specializations/deeplearning?
1 points • reckless_commenter
They don't have to be. Last summer, I completed Andrew Ng's Coursera series on deep learning  100% recorded videos and automatically graded Jupyter notebooks. I learned a ton.
1 points • killerall5385
https://www.coursera.org/specializations/deeplearning
1 points • willspag
https://www.coursera.org/specializations/deeplearning
Take this and you’ll be kicking ass in no time. Takes you from beginner to low level expert and makes sure you learn all the details in between
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 • LtSkeletonSFW
Andrew's courses are pretty good, and the [Deep Learning Specialization] (https://www.coursera.org/specializations/deeplearning) cource is a good continuation.
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 • 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.
https://www.coursera.org/specializations/deeplearning
1 points • konddmy
Actually it’s quite googlable: https://www.coursera.org/specializations/deeplearning.
1 points • its_all_relative_
Do this: https://www.coursera.org/specializations/deeplearning