Intro to TensorFlow for Deep Learning

Deep learning free course on Udacity - Built by Google

Best deep learning course after ML course?

Hi, I recently completed a machine learning course in university that covered all the basics including regression, SVMs, kernels, boosting, clustering, and touched on neural networks and deep learning. I found it really interesting but I want to get some more applicable knowledge and experience (with deep learning specifically) as I'll be starting at Google in a few months. I've found lots of resources online but I was wondering if you guys had any input on which would be the best path to take. I want to get some experience with TensorFlow, but I don't have a lot of experience with python, my course used Matlab.

Here's what I'm considering:

• CS231n: Convolutional Neural Networks for Visual Recognition

• CS224n: Natural Language Processing with Deep Learning

• Udacity - Deep Learning by Google

Would anyone be able to vouch for any of these courses, or make any recommendations given my situation and background? Or if you have any other course suggestions I'd be happy to hear them. I'm eager to get started but just want to make sure I'm going down the best path. Thanks!

Deep Learning - Taking machine learning to the next level (Free Course)

Google's Free Deep Learning Course

Try this free Udacity course which does a good job introducing you to coding them

Google Deal with Movidius (from Project Tango) + Free Udacity Online Deep Learning Course (for advance developers)

It looks like VR's about to get a bit more high tech. They worked with this company on Project Tango. Now, they made a deal with them for smartphones.

That explains the free 'Deep Learning' course.

http://www.engadget.com/2016/01/28/google-movidius-partnership/

https://www.reddit.com/r/oculus/comments/434gin/google_announces_vision_processing_chip_to_be/

Movidius Vision Processing Unit http://www.movidius.com/solutions/vision-processing-unit

Udacity https://www.udacity.com/course/deep-learning--ud730

udacity Deep Learning

Introduction to Deep Learning: a free, 3-month (estimated; self-paced) online course

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?

Thanks(:

Do you need a laptop with good GPU to make good use of tensorflow?

Hi all,

I'm plan to go through the entire https://www.udacity.com/course/deep-learning--ud730 course. The above course does make use of TensorFlow

Recently, my old laptop spoiled, and I'm going to get a ThinkPad X1 Carbon (5th gen)

I'm an Android developer by profession, so the above laptop will suit my need.

However, I understand that for deep learning, using TensorFlow, might require a lot of GPU processing.

The above laptop comes with Intel® HD Graphics 620.

Since it doesn't come with a dedicated GPU (Like NVIDIA), I was wondering will I face any difficulty while going through the course & having obstacle when developing some useful real-world application using deep learning technique?

If so, I might change my laptop purchasing plan.

Thanks.

As far as the first bit, learning python's pretty easy and courses like https://www.udacity.com/course/deep-learning--ud730 make it easy to piece together algorithms to analyze data using TensorFlow. Governments have been really slow to adapt (if they've even started) to the recent advancements in hardware that allow you to run these in a reasonable amount of time at home/cheaply in the cloud so I'm not sure you're at that much of a disadvantage.

I'm interested in deep learning and I've finished Andrew Ng's course. Which of these 3 online courses would be best to try next?

1. Deep Learning at Udacity

2. Deep Learning in Python at Udemy

3. Practical Deep Learning in Theano + TensorFlow at Udemy

4. Neural Nets at Coursera

5. Convolutional Neural Networks for Visual Recognition

I'm aware that Kaggle and personal projects are important as well, but this question is just about the courses. Does anyone who has completed any of these have feedback? Thanks.

Edit: Added a 4th candidate that I found

Edit 2: Since people are recommending Karpathy's course, adding it here as a reference. It's not a 'native online' course, but who cares?

Teachers have to know deeplearning in order to teach deeplearning. And deeplearning is a new field, rapidly evolving, Lots of research is going on. And basic deeplearning requires knowledge in linear algebra.

There an online corse backed by google. But from what I hear it's an advanced corse.

Best starting resource i've found is probable Siraj Raval on youtube .

Deep learning is a kind of machine learning (ML) that typically uses deep neural networks (i.e. networks with many layers / indirection between input and output).

One common task in machine learning is classification: saying to which class(es) a particular input pattern belongs. For instance, we could classify facial expressions from pictures of faces. "Features" are things we can see in those pictures. These can include things like pixel (colors), lines, average angles, noses, eyes, and indeed faces and facial expressions themselves. We think of pixels as very low-level features, while facial expressions are much higher-level features.

In order to classify facial expressions from pictures, pre-deep-learning ML algorithms could usually not just use the pixels as inputs. Instead, humans came up with feature extraction algorithms for this task, which allowed them to feed higher-level features into their (shallow) neural networks. So you might say that deep learning is deep because it can deal with a larger "distance" between (low-level) inputs and (high-level) classes.

There are some resources for learning more on the sidebar, wiki and here:

• http://deeplearning.net/
• https://www.deeplearning.ai
• https://www.udacity.com/course/deep-learning--ud730
• http://www.deeplearningbook.org/

Google, as the video suggests is a huge powerhouse in machine learning right now. From offering free courses in machine learning (which i've been told are quite good). To their many. cool. experiments. Not to mention providing the gold standard in machine learning libraries.

But the interesting thing is these technologies give an undeniable edge in any business sense and rather than trying to stifle competition they are providing this freely. Seemingly they have decided that the altruist path must benefit them somehow. Either they just want to see the technology expand or just try and grow the amount of AI programmers for potential hires.

/u/zionsrogue, it's not just the highest tier, but every single tier.

What I think each of these comments on this reddit page is trying to convey is that the building blocks in this field, which I am sure you heavily drew from, were/are fundamentally free.

To cite the top 5 resources in this field of Deep Learning in general with a focus on Vision: 1. http://cs231n.stanford.edu/ 2. http://www.deeplearningbook.org/ - the 'godfather' book of DL 3. http://neuralnetworksanddeeplearning.com/ 4. Udacity course delivered by Google - UD730 https://www.udacity.com/course/deep-learning--ud730 5. Siraj Raval's Deep Learning series on YouTube

The general theme here seems to be that you are not justifying how you provide substantially greater value over and above the free resources or even the moderately priced ones by one of the greats in this field - like https://www.deeplearning.ai/ by Andrew Ng for what you are charging - $145, $295 and (whoa!) $645, which on face value, appear to be exorbitant.

Heck, for this level of money, you don't even touch Tensorflow or PyTorch in your book which are the platforms on which most serious DL work happen.

RE: instructions around replicating state-of-the-art publications, aren't those publications written to specifically provide this instruction along with the accompanying datasets.

Obviously, it is a free country and you are free to charge whatever you choose to, (and congratulations for raising $262,792 via kickstarter for this package, btw), but it just rankles when your offering looks like a crass commercialism play built on the backs of the selfless initial set of people who shared this knowledge freely that you are simply re-packaging and selling to your blog audience who are probably unaware of the other, free/more reasonably priced resources out there.

Of course, I could be wrong and you are revealing some major truths or making it significantly easier for people to on-ramp onto DL which the other resources do not, in which case all of us would love to know how you do so, so that we have a better understanding of your offering and try to justify the asking price against these features.

https://www.udacity.com/course/deep-learning-pytorch--ud188

edit: I can almost bet these will be part of the challenge course too https://www.udacity.com/course/deep-learning--ud730

https://www.udacity.com/course/machine-learning--ud262

All the challenge course lessons are free.