From 21ff3b20f7d810bca63fc125f6a372c5256db95f Mon Sep 17 00:00:00 2001 From: mnassar Date: Sat, 6 Jun 2020 12:04:46 +0300 Subject: [PATCH] Update readme --- README.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 3c2679c..49229ef 100644 --- a/README.md +++ b/README.md @@ -4,15 +4,14 @@ The Deep Learning _Handbook_ is a project in progress to help study the [Deep Le Goodfellow's masterpiece is a vibrant and precious resource to introduce the booming topic of deep learning. However, many found the accompanying video lectures, slides, and exercises not pedagogic enough for a fresh starter. -I used the **'dlbook'** for the class that I have taught in Spring 2019/2020 at the computer science department, American University of Beirut. +I used the **'dlbook'** as the primary reference for the machine learning class that I have taught in Spring 2019/2020 at the computer science department, American University of Beirut. I would like to share my experience by publishing the slides and some of the assignments on this page. The project may be developed further into a full handbook and guide accompanying the fascinating text of Goodfellow et al. -The target audience comprises: The target audience comprises: * undergraduate and graduate students willing to know more about machine and deep learning, be able to read research papers or start a graduation project or a master thesis in the same context, -* developers and practitioners wanting to aspire a bit more math and philosophy, -* or mathematicians liking to have a bit more coding experience, +* developers and practitioners aspiring to a bit more math and philosophy, +* or mathematicians liking to have some hands-on and a bit more coding experience, * any other bored or sleepless person. Currently, only part I and part II are covered. @@ -741,7 +740,7 @@ plt.plot(t, np.log(1+np.exp(t)), 'g-') #### (b) Similar to the exercise above (gradient-based learning), design a neural network that learns the 3 means of a gaussian mixture with 3 components * Assume $x$ is one dimensional -* {% raw %} $p(y|x)$ {% endraw %} is a gaussian mixture of three components +* {% raw %} $ p(y\|x) $ {% endraw %} is a gaussian mixture of three components * Assume that the three components are equally likely * Assume all variances are 1 @@ -1424,7 +1423,7 @@ for x in advs: ![png](exercises/images/CMPS_392_Asst_6_Regularization_11_2.png) -## Ex3 +## Ex 3 ### Noise Robustness vs. dropout in the special case of linear regression (a) Show that adding Gaussian noise with **small** magnitude to the weights and biases of linear regression (the noise has mean $0$ and variance $\eta << 1$) does not affect the solution of the gradient descent. @@ -1499,7 +1498,7 @@ print (model.weight) tensor([[0.5496, 0.4467, 0.0899]], device='cuda:0', requires_grad=True) -## Ex4 +## Ex 4 ### L2 regularization vs. L1 regularization (a) Based on the data provided below, design an experiment to show the difference between the solutions to three **logistic regression** problems: