<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[王彩旗的技术博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/edward_wang1</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; edward_wang1]]></copyright><item><title><![CDATA[Ian Goodfellow]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/119248695</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/119248695</guid><author>edward_wang1</author><pubDate>Sat, 31 Jul 2021 11:57:09 +0800</pubDate><description><![CDATA[CUDA
	CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach term]]></description><category></category></item><item><title><![CDATA[Pieter Abbeel]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/119172349</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/119172349</guid><author>edward_wang1</author><pubDate>Wed, 28 Jul 2021 12:47:08 +0800</pubDate><description><![CDATA[deep reinforcement learning
	reinforcement leraning
	autonomous helicopter flight
	ImageNet (Geoffrey Hinton, Toronto team)
	AlexNet
	Supervise learning is about learning an input and output mapping
	Reinforcement learning: where does the data even come fr]]></description><category></category></item><item><title><![CDATA[Geoffrey Hinton]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/119136295</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/119136295</guid><author>edward_wang1</author><pubDate>Tue, 27 Jul 2021 11:52:56 +0800</pubDate><description><![CDATA[The notes for interviewing Geoffrey Hinton, the godfather for deep learning, by Andrew Ng. You can definitely see many advanced techniques in deep learning and some good advice for how to start with your deep leraning!
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Restricted Boltzman]]></description><category></category></item><item><title><![CDATA[Deep Neural Networks - Parameters vs Hyperparameters]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/119085026</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/119085026</guid><author>edward_wang1</author><pubDate>Sun, 25 Jul 2021 18:58:58 +0800</pubDate><description><![CDATA[Being effective in developing your deep NN requires that you not only organize your parameters well, but also your hyper parameters. So, what are hyper parameters?

Parameters



Hyper parameters

Learning rate : it determines how the parameters evolve
	N.]]></description><category></category></item><item><title><![CDATA[Deep Neural Networks - Building blocks of deep neural networks]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118996581</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118996581</guid><author>edward_wang1</author><pubDate>Thu, 22 Jul 2021 12:50:44 +0800</pubDate><description><![CDATA[Take the deep NN of figure-1 as the example:

Figure-1Following figure-2 shows the building blocks of this deep NN while generalized the figure for NN with L layers:

Figure-2






]]></description><category></category></item><item><title><![CDATA[Deep Neural Networks - Forward Propagation in a Deep Network]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118959831</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118959831</guid><author>edward_wang1</author><pubDate>Wed, 21 Jul 2021 10:10:30 +0800</pubDate><description><![CDATA[Take a deep NN in figure-1 as an example,

Figure-1

For a single training example x




Concretely,

















For multiple (m) training examples

]]></description><category></category></item><item><title><![CDATA[Deep Neural Networks - Deep L-layer Neural network]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118945776</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118945776</guid><author>edward_wang1</author><pubDate>Tue, 20 Jul 2021 22:30:53 +0800</pubDate><description><![CDATA[The notes when study the Coursera class by Mr. Andrew Ng "Neural Networks &amp; Deep Learning", section 4.1 "Deep L-layer Neural network". It shows what deep NN looks like and notations to denote and compute NN. Share it with you and hope it helps!
———————]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Random Initialization]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118669730</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118669730</guid><author>edward_wang1</author><pubDate>Mon, 12 Jul 2021 10:51:19 +0800</pubDate><description><![CDATA[When you train your NN, it's important to initialize the weights ( etc.) randomly. For logistic regression, it's ok to initialize the weights to 0; but for NN, if initialize the weights all to 0 and then apply gradient descent, it won't work!


]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Gradient descent for neural networks]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118668177</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118668177</guid><author>edward_wang1</author><pubDate>Mon, 12 Jul 2021 10:09:06 +0800</pubDate><description><![CDATA[The notes when study the Coursera class by Mr. Andrew Ng "Neural Networks &amp; Deep Learning", section 3.9 "Gradient descent for neural networks". It shows the computation graph for NN, how to compute back propagation of NN when there is one and multiple ]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Derivatives of activation functions]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118602879</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118602879</guid><author>edward_wang1</author><pubDate>Fri, 09 Jul 2021 12:15:39 +0800</pubDate><description><![CDATA[When you implement back propagation for your NN, you need to compute the slop/derivative of the activation function. Let's take a look at how to compute the slope of those activation functions.

Sigmoid function
figure-1We have:



Following is the deriv..]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Activation functions]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118544003</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118544003</guid><author>edward_wang1</author><pubDate>Wed, 07 Jul 2021 12:29:59 +0800</pubDate><description><![CDATA[W W  W

When build a NN, one of the choices to make is what activation functions to use in the hidden layers as well as the output layer. Besides the sigmoid activation function, sometimes other choices can work much better.

tanh function
Als...]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Vectorizing across multiple examples]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118519290</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118519290</guid><author>edward_wang1</author><pubDate>Tue, 06 Jul 2021 15:33:04 +0800</pubDate><description><![CDATA[The notes when study the Coursera class by Mr. Andrew Ng "Neural Networks &amp; Deep Learning", section 3.4 "Vectorizing across multiple examples". It shows how to compute NN output via vectorization when there are multiple training example. Share it with ]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Computing a Neural Network‘s Output]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118490051</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118490051</guid><author>edward_wang1</author><pubDate>Mon, 05 Jul 2021 15:09:03 +0800</pubDate><description><![CDATA[Let's see how the Neural Network computes its output. It's like logistic regression, but repeat a lot of times!

figure-1Figure-1 shows how to compute the output of logistic regression.





Figure-2 shows how to compute the activation units of hidden la..]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Neural Network Representation]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118487848</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118487848</guid><author>edward_wang1</author><pubDate>Mon, 05 Jul 2021 14:45:55 +0800</pubDate><description><![CDATA[The notes when study the Coursera class by Mr. Andrew Ng "Neural Networks &amp; Deep Learning", section 3.2 "Neural Network Representation". Share it with you and hope it helps!

------------------

figure-1
Figure-1 shows names of different parts of Neura]]></description><category></category></item><item><title><![CDATA[One hidden layer Neural Network - Neural Networks Overview]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118487505</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118487505</guid><author>edward_wang1</author><pubDate>Mon, 05 Jul 2021 12:45:41 +0800</pubDate><description><![CDATA[Let's give a quick overview of how you implement your neural network.

figure-1 logistic regressionFigure-1 shows how we compute logistic regression using computation graph.

figure-2 neural networkFigure-2 shows what the Neural Network looks like. We'll u]]></description><category></category></item><item><title><![CDATA[Basics of Neural Network Programming - Vectorizing Logistic Regression and its Gradient Computation]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118147387</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118147387</guid><author>edward_wang1</author><pubDate>Wed, 23 Jun 2021 11:49:32 +0800</pubDate><description><![CDATA[Let's talk about how to vectorize the implementation of logistic regression. With that we can implement a single iteration of gradient descent with respect to entire training set without using even a single explicit for loop.

Figure-1In gradient descent, ]]></description><category></category></item><item><title><![CDATA[Basics of Neural Network Programming - More vectorization examples]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118113241</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118113241</guid><author>edward_wang1</author><pubDate>Tue, 22 Jun 2021 18:57:22 +0800</pubDate><description><![CDATA[The rule of thumb to keep in mind is when you programming your neural networks or logistic regression, whenever possible, avoid explicit for loops. Let's look at another example.

Figure-1Figure-1 shows two different ways to compute  where u and v are vec.]]></description><category></category></item><item><title><![CDATA[Basics of Neural Network Programming - Vectorization]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118112274</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118112274</guid><author>edward_wang1</author><pubDate>Tue, 22 Jun 2021 17:55:52 +0800</pubDate><description><![CDATA[Vectorization is the art to get rid of the for loops in the code. The ability to perform vectorization has become a key skill.

Figure-1















Figure-1 shows two different ways to calculate
]]></description><category></category></item><item><title><![CDATA[Basics of Neural Network Programming - Gradient descent on m examples]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118093675</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118093675</guid><author>edward_wang1</author><pubDate>Mon, 21 Jun 2021 20:38:10 +0800</pubDate><description><![CDATA[Last class, you saw how to compute derivatives and implement gradient descent with respect to just one training example for logistic regression. Now, we'll do it for m training examples.

Recap the cost function of logistic regression:



And,



Accordin.]]></description><category></category></item><item><title><![CDATA[Basics of Neural Network Programming - Logistic Regression Gradient descent]]></title><link>https://blog.youkuaiyun.com/edward_wang1/article/details/118093009</link><guid>https://blog.youkuaiyun.com/edward_wang1/article/details/118093009</guid><author>edward_wang1</author><pubDate>Mon, 21 Jun 2021 19:34:00 +0800</pubDate><description><![CDATA[This is the notes when study class Neural Networks &amp; Deep Learning, section Logistic Regression Gradient Descent. Share it with anyone who is interested in.


Following two figures shows how to calculate derivatives for logistic regression when one tra]]></description><category></category></item></channel></rss>