backpropagation


The Simplest Neural Network: Understanding the non-linearity

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The first neural network you want to build using squaring of numbers. Every time you want to learn about NNs or data science or AI, you search through google, you go through Reddit, get some GitHub codes. There is MNIST dataset, GANs, convolution layers, everywhere. Everybody is talking about neural networks. You pick up your laptop, run the code, Voila! it works.


A roadmap to understanding Neural Networks

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'What I cannot create, I do not understand' -- Richard Feynman This quote has haunted for the majority of this year. For the last few months, I had been feeling like an impostor. I felt that my reliance on high-level deep learning libraries had been an impediment to me fully understanding neural networks and what goes on under the hood. So, in May 2019 I decided to give up high-level libraries such as Tensorflow and not use them until I could write a neural network from scratch using only low lying mathematical and numerical operation libraries such as Numpy in Python. As I set out to do this, I went to our trusty friend Google and I was overwhelmed by the sheer number of resources.


Neural Networks: Is Meta-learning the New Black?

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I've written several times about how the "economies of learning" are more powerful than the "economies of scale". Through a continuous learning and refinement process, organizations can simultaneously drive down marginal costs while accelerating time-to-value and de-risking projects via digital asset re-use and refinement (see Figure 1). Think about how us lowly humans learn. Whether trying to hit a golf ball or playing the piano or water skiing, we learn though the "feedback loop of failure". And the more real-time, immediate that feedback loop, the more quickly we can assess what we did wrong, adjust and then try again.


CNN Heat Maps: Gradients vs. DeconvNets vs. Guided Backpropagation - WebSystemer.no

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This post summarizes three closely related methods for creating saliency maps: Gradients (2013), DeconvNets (2014), and Guided Backpropagation (2014). Saliency maps are heat maps that are intended to provide insight into what aspects of an input image a convolutional neural network is using to make a prediction. All three of the methods discussed in this post are a form of post-hoc attention, which is different from trainable attention. Although in the original papers these methods are described in different ways, it turns out that they are all identical except for the way that they handle backpropagation through the ReLU nonlinearity. Please stay tuned for the next post, "CNN Heat Maps: Sanity Checks for Saliency Maps" for a discussion of a 2018 paper by Adebayo et al. which suggests that out of these three popular methods, only "Gradients" is effective.


Matrix Sketching for Secure Collaborative Machine Learning

arXiv.org Machine Learning

Collaborative machine learning (ML), also known as federated ML, allows participants to jointly train a model without data sharing. To update the model parameters, the central parameter server broadcasts model parameters to the participants, and the participants send ascending directions such as gradients to the server. While data do not leave a participant's device, the communicated gradients and parameters will leak a participant's privacy. Prior work proposed attacks that infer participant's privacy from gradients and parameters, and they showed simple defenses like dropout and differential privacy do not help much. To defend privacy leakage, we propose a method called Double Blind Collaborative Learning (DBCL) which is based on random matrix sketching. The high-level idea is to apply a random transformation to the parameters, data, and gradients in every iteration so that the existing attacks will fail or become less effective. While it improves the security of collaborative ML, DBCL does not increase the computation and communication cost much and does not hurt prediction accuracy at all. DBCL can be potentially applied to decentralized collaborative ML to defend privacy leakage.


Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

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So, using this new information let's add another node to a neural network; the bias node. Now let's do a forward propagation with the same example, x₁ 0, x₂ 0, y 0 and let's set bias, b 0 (initial bias is always set to zero, rather than a random number), and let the backpropagation of Loss figure out the bias. Well, the forward propagation with a bias of "b 0" didn't change our output at all, but let's do the backward propagation before we make our final judgment. As before let's go through backpropagation in a step by step manner. Since the derivative of bias( L/ b) is positive 0.125, we will need to adjust the bias by moving in the negative direction of the gradient(recall the curve of the Loss function from before).


Basics of AI - Backpropagation (Video)

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This is a superb and very simple explanation of one of the basic concepts of AI. This should be included in most advanced math curriculum if only to demystify what AI really is. It also explains why AI can now distinguish dogs from cats or recognize individuals.) Source: End to end machine learning library.


On Education Natural Language Processing with Deep Learning in Python - all courses

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Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GloVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to derive and code the equations on your own Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function Code a feedforward neural network in Theano (or Tensorflow) Helpful to have experience with tree algorithms In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.


On Education Natural Language Processing with Deep Learning in Python - all courses

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Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GloVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to derive and code the equations on your own Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function Code a feedforward neural network in Theano (or Tensorflow) Helpful to have experience with tree algorithms In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.


On Education Data Science: Deep Learning in Python - all courses

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This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.