Introduction to Deep Learning: Machine Learning vs Deep Learning

24 thoughts on “Introduction to Deep Learning: Machine Learning vs Deep Learning”

1. Great!

2. Very good video.

3. Simple and effective explanation…thx

4. This is only the beginning. When computers can start programming themselves and heuristically and recursively, and iteratively learn it will be at thousands of times faster than we can.

5. This was the best kind of Advertisement, one that's also genuinely informative.

6. Short and sweet to the point thanks

7. Is deep learning the same as unsupervised learning?

8. i found a much faster way to do machine learning than neural net. one that is shit stupid, and don't do more work than is needed to make out the features of an image and by combineing two input of information lets say picture and sound i can make the computer selflearn unsupervised. my algorithm was first an idea to make a selfdriving car that uses calculations to keep itself on the road on a desired side by modyfing its data on the fly without being able to remember anything. the method i describe can be used to create memories storied in arrays and married to works as learning. its like creating a single celled life form that can learn. the trick behind my method is to store the target image inside a numerical array, then render the array on the screen then create diagonal rays sweep left and right in the array at many angles to detect when sameness of pixels changes to non-sameness and use that to determine the distance traced on each side left and right at many angles and then store these numbers inside an array the represent the image, then do this with let say sound then combine sound and image array as a memory, so that when it recalls let say by listening to the same sound, it will show the array example of the image it extracted the information from as corresponding to the sound. so in my method i use dicrapencies in pixel colors and counting distance by how many pixels that repeat the same type until it reach a difference in the colors to measure the distances from left to right of the object its recognizing so that it can draw a crude copy of the outline of the target image and store the numbers in an array serving as a memory. if you mess up the colors lets say on a dress of a person, my computer program will not be able to recognize you outline correctly. the advantage with my method is that it can store hundres of patterns in arrays of only 8 numbers for each line therby taking up very little numbers of bytes. my idea is to create a process that cheats, takes little space and is fast. the neural net method on the other hand is slow takes a lot of space do to many trial and error operations and need a lot of computer power, not suitable for cheap microcontroller. neural net is a great, its fantatic, its just the its to power demanding and mostly for rich people or companies. i love to see progress in neural networks and perhaps even a evolution engine that mutates neural networks by natural selaction. my approach was to find a way to code a lot of information in a impossible small space, and there was no room for such a slow and heavy system as a neural net on these chips. the next evolution of computers algoritms must be to outsmart the ingenuity of nature by creating stuff on small space using little resource to do a massive job far ahead of a natural system, and not one that use even more resources and power than the natural system. its supposed to be that human intelligence was creating a artificial intelligence that was smarter than what the little molecular machines could do by combining random with order chemical reactions changing their equalibrium on a resource based computational basis. we supposed to create algoritms that outperforms the need of resources that the natural system uses by doing the algoritms smarters than natural process by using the combined intelligence of the brain to beat the molecular machinery and not creating a system that underperforms nature. we need to create a intelligece equal to the cell level that is smarter than the cell in order to make a network of this to outperform the brain. its not the complexity of the human brain that makes intelligence but the way the system is wired and the way the algoritms works than in a multiplied sense define the whole. maybe the genius programmer do exist, maybe there is many of them, they just have not gotten far enough in advances to acomplish the impossible. the collective intelligence idea is definetly an efficient one. i think both senarios works equally well but with different rules in the way of making it. one method is to do evolution with one guy or god and let the program evolve its own advancements, the other is to use humans to collectivly andvace a program by using human intelligence to solve all the problems. i think if the universe was created by one programmer, then it must have been done by a evolution engine where godly creation and evlotion is one and the same. the progrmmer create the evolution program that drives itself based on the rule set made by the programmer.

9. Deep leaning algorithms are also written by humans then why is it called "Black Box"? Can't we look into it and see how the computer solved it?

10. Python ❤️

11. Thanks!! Very informative and objective. Just what I needed

12. Excellent explanation, thanks.

13. I want to classify a training data that doesn't depend on images using deep learning how can this be ?

14. Very clear and to the point. Great job!

15. Does this seem ridiculous to anyone else? Deep learning is a subset of machine learning as far as i know. 'When you are doing deep learning you stop designing features and you need more data'…. uhhh ya…??? 'With deep learning you skip the step of manually extracting features'… uhhh how so? just for image classification or in general? I thought you still need to feed features to networks… do I have a major misunderstanding or is this silly?

16. great !

17. Good

18. Machine Learning = Supervised Classification; Deep Learning = Unsupervised Classification… in Remote Sensing.

19. Does a Haar cascade classifier by opencv belong to DL or ML.

20. this explanation have made me even more confused ><

21. She leads us to think that deep learning is only catered for image-based problems.

22. why matlab researchers are so serious?
    It's a clickbait, she was laughing in the thumbnail.

23. #tilt it

24. Very crisp..but effective. Thanks!

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