Tutorial 5- How to train MultiLayer Neural Network and Gradient Descent

In this video we will understand how to train a multiLayer Neural Network with Backpropagation and Gradient Descent
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You SO FAR MY BEST DEEP LEARNING TEACHER GOD BLESS YOU

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Great tutorial I have ever found in any platform. Even we can't get such a quality learning after paying a huge amount. It's an get opportunity you have provide for those who want to master the data science skills. Thanks you so much Krish. Appreciate your hard work. Do keep us updated with latest data science skills.

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Sir the vedios are really very helpful. Please cover each and every topic related to Neural Network. Thank you .

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Yes, ur lecture are good , but need more insight with example.....

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Great explanation. I have never heard like this type of class. Watchers are blessed.

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I am sure you are great ML AI engineer. This video confirmed that you are an awesome teacher -especially for the applied maths/calculus. I am certain that you can make a boring theory maths or Stats college class turn into an interesting busy class. Thank you Krish.

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You are a blessing to the Data Science community! Thank you for all your guidance!

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What nice about these videos and the presenter is great in sight in explaining and drawing on board which is excellent instead of boring ppts and visuals.

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the best videos on gradient descent i've ever seen. you are juste amazing. god bless you.

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When the going gets tough the tough gets going.Explanation is fabulous but need to keep track of things and not deviate surely .Thanks

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now I understand Gradient Descent. Thank you sir

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I watched all the videos on this particular topic this one is best and simplest explained on whole yt GBU❤

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One of the best video i have ever watched in youtube for learning how does gradient Descent work. How the parameters are updated. Thank you so much sir for your great effort. Sir it would great if you could so some practical implementation.

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one of the best explanations so far.I dont know why profs make it complex for people to learn these concepts.Thank you so much for such a great effort.

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Kudos Krish!! Thank you is not enough to convey the gratitude...

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[@dcrespin]

For BPP, deep learning and for the general structure of neural networks the following comments may be useful. To begin with, note that instead of partial derivatives one can work with derivatives as the linear transformations they really are. It is also possible to look at the networks in a more structured manner. The basic ideas of BPP can then be applied in much more general cases. Several steps are involved. 1.- More general processing units. Any continuously differentiable function of inputs and weights will do; these inputs and weights can belong, beyond Euclidean spaces, to any Hilbert space. Derivatives are linear transformations and the derivative of a neural processing unit is the direct sum of its partial derivatives with respect to the inputs and with respect to the weights; this is a linear transformation expressed as the sum of its restrictions to a pair of complementary subspaces. 2.- More general layers (any number of units). Single unit layers can create a bottleneck that renders the whole network useless. Putting together several units in a unique layer is equivalent to taking their product (as functions, in the sense of set theory). The layers are functions of the of inputs and of the weights of the totality of the units. The derivative of a layer is then the product of the derivatives of the units; this is a product of linear transformations. 3.- Networks with any number of layers. A network is the composition (as functions, and in the set theoretical sense) of its layers. By the chain rule the derivative of the network is the composition of the derivatives of the layers; this is a composition of linear transformations. 4.- Quadratic error of a function. ... --- Since this comment is becoming too long I will stop here. The point is that a very general viewpoint clarifies many aspects of BPP. If you are interested in the full story and have some familiarity with Hilbert spaces please google for papers dealing with backpropagation in Hilbert spaces. A related article with matrix formulas for backpropagation on semilinear networks is also available. For a glimpse into a completely new deep learning algorithm which is orders of magnitude more efficient, controllable and faster than BPP search in this platform for a video about deep learning without backpropagation; in its description there are links to a demo software. The new algorithm is based on the following very general and powerful result (google it): Polyhedrons and perceptrons are functionally equivalent. For the elementary conceptual basis of NNs see the article Neural Network Formalism. Daniel Crespin

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Very nice way of explaining, waiting for more videos on deep learning. Thank you for this series.

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Showing the x axis as "weights" and y axis as "loss", help so much to understand the consept of GD optimizer!

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