Deep Learning

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.

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?

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.

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.

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/deep-learning

Your Thoughts on Coursera's Deep Learning Specialization with Andrew Ng?

https://www.coursera.org/specializations/deep-learning

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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.

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.

Take the coursera deep learning specialization: https://www.coursera.org/specializations/deep-learning

The new Andrew Ng deep learning course is this? --> https://www.coursera.org/specializations/deep-learning ??

Andrew Ng's deep learning course covers the fundamentals pretty well IMO:

https://www.coursera.org/specializations/deep-learning

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.

Even among engineers/computer scientists there are varying levels of math ability. Linear algebra, probability theory, and discrete math come to mind as topics almost everyone studies with a CS background.

I would recommend knowing the basics of any CS undergraduate degree as a start, but there are many different areas where a deeper foundation in math can be useful. I.e. advanced statistics, differential equations, etc. These may or may not be useful to you depending on what you plan on doing.

Also start at looking at your interests. For example, SOTA on machine translation is largely neural, so you’ll want to have enough math background to handle a standard course on machine learning/deep learning. You can take a look at Andrew Ng’s deep learning course and see which topics you understand or don’t understand. That can also guide your learning.

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

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.

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.

Are you perhaps working for Perceptive Automata? Either way, I commend you on your bravery to tackle a problem in a space you are not familiar with. That being said, given the safety-critical nature of this application, If you are the task lead developing something field-ready that will actively make safety decisions, would implore you to consult deeply and at length with other senior engineers in the space before moving forward with trying to design a system. The last thing you'd want is to have an incomplete understanding of the behaviors and failure modes of the systems you're implementing. I normally would just say "Go get 'em!" in times like this, but given the context of how critical this deployed system will be, I hesitate.

Now that I have that out of the way, I'd recommend any course by Andrew NG - I've always loved his courses:
https://www.coursera.org/specializations/deep-learning?utm_source=gg&utm_medium=sem&utm_campaign=17-DeepLearning-US&utm_content=17-DeepLearning-US&campaignid=904733485&adgroupid=45186268225&device=c&keyword=deep%20learning%20certification%20online&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=415503611672&hide_mobile_promo&gclid=Cj0KCQjw1dGJBhD4ARIsANb6OdlbMOWdgjks-nWGd5RJiKDUVy8JNio8GmXYQPjXP4tXo3CG3NU0ulwaAhkLEALw_wcB

Stay safe out there, and best of luck to you!

https://www.coursera.org/specializations/deep-learning

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/deep-learning

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!

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/deep-learning?utm_source=deeplearningai&utm_medium=institutions&utm_campaign=WebsiteCoursesDLSTopButton

Thanks for your opinion, appreciate it. Here's the course for those interested: https://www.coursera.org/specializations/deep-learning

I think Deep Learning Specialization covers many important topics that could come up in interviews.

I'd recommend Deeplearning.ai's Specialization on Coursera, it has great explanations and hands on assignments.

This one:

https://www.coursera.org/specializations/deep-learning

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:

1. 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.
2. 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.
3. 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.

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/deep-learning

Fastai https://course.fast.ai/

https://www.coursera.org/specializations/deep-learning https://www.coursera.org/specializations/tensorflow-in-practice

I highly suggest this course. It will teach you enough

https://www.coursera.org/specializations/machine-learning

You're going for an entry level position, you're not going to be competitive for crazy data jobs with fancy deep learning algos from scratch, Bayesian Methods, or reinforcement learning. You need a project that shows you understand the basics. Someone is going to flip through it in about 5 minutes. If you're lucky you'll have 15 minutes to step through it in a phone screen. You need to show you understand data cleaning and imputation, cross validation, loss functions, evaluation metrics, and can think about what your results mean and where to go next. Classic regression algos, trees, KNN, and PCA are enough. Make sure you can use pandas and SQL fluently. You may get asked about deep learning basics. You can do unit 1 from this course (or all of it if you enjoy learning)

https://www.coursera.org/specializations/deep-learning

But consider the scope of the job postings you apply to. It's mostly going to be regression problems and data cleaning at entry level. Maybe if you're lucky you do well on an interview and you are given the opportunity to learn something cool at work, but they won't expect it going in. Show them you know the basic concepts.

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/deep-learning) or the fast.ai series (http://www.fast.ai/). I think the fast.ai course is totally free to follow along.

Here you go my friend! https://www.coursera.org/specializations/deep-learning

Have a look at this online course by Andre Ng: https://www.coursera.org/specializations/deep-learning

Includes videos, homeworks, quizzes, etc.

Vejo muito o pessoal começando com essa série aqui (são 5 cursos): https://www.coursera.org/specializations/deep-learning?

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.

Andrew Ng's Coursera course is free if you don't care about projects, and the lectures are solid https://www.coursera.org/specializations/deep-learning

Hey, thanks for your response. I have recently started deep learning specialization by Andrew Ng on Coursera. https://www.coursera.org/specializations/deep-learning And also I will be starting a couple of projects soon.

Best online course about Deep Learning

I’m looking for a deep learning online course, what do you think about these?

https://www.coursera.org/specializations/deep-learning (quite new) https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Thanks

ng has a few courses on coursera. consider this specialization: https://www.coursera.org/specializations/deep-learning. the math specialization would be good also.

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.

I strongly recommend these (free) courses by Andrew Ng, a brilliant computer scientist and a pioneer of machine learning.

https://www.coursera.org/specializations/deep-learning?

I do recommend the deep learning specialization from coursera ( https://www.coursera.org/specializations/deep-learning ). Additionally, they have updated this specialization last month with new content, so I think you should give it a try or at least check it (and also if you enjoyed ML course, I think you will enjoy this one too).

https://www.coursera.org/specializations/deep-learning

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

Do this: https://www.coursera.org/specializations/deep-learning

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!

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.

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.)

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):

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. 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.

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

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 !!

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

Andrew's courses are pretty good, and the [Deep Learning Specialization] (https://www.coursera.org/specializations/deep-learning) cource is a good continuation.

Για deep learning είναι και αυτό; https://www.coursera.org/specializations/deep-learning

Από UK: Γενικά ο τομέας μεταβάλλεται συνεχώς με ειδικότητες να βγαίνουν κάθε λίγα χρόνια. Certificates δεν αξίζουν αν ξεκινάς τώρα γιατί δεν έχεις δει τι σου αρέσει για να εξειδικευτεις από τώρα. Καλό για αρχή είναι να ξέρεις λίγο απ'ολα. Πχ αν κανείς πρότζεκτ μόνος σου κοίτα να κάνεις λιγο NLP, λίγο computer vision, λίγο time series κτλ. Μην επικεντρώνεσαι σε ένα πράγμα. Αυτό που αξίζει είναι στα project που κάνεις έστω και μόνος να τα έχεις στο GitHub public (κ στο CV το προφίλ σου) ώστε να μπορεί να δει κανείς.

Μικρή ή μεγάλη εταιρεία δεν έχει τόση σημασία γιατί περισσότερο μετράει ο ρόλος κ οι συνεργάτες. Γι'αυτό ρώτα συγκεκριμένα πράγματα για τις αρμοδιότητες σου κατά τις συνεντεύξεις. Βέβαια αν πας σε σταρτ απ έχει περισσότερες πιθανότητες να έχει πίεση.

Ho visto che ti interessi di data science. La specializzazione in Deep Learning è fatta molto bene