forked from neonwatty/machine-learning-refined
-
Notifications
You must be signed in to change notification settings - Fork 0
/
toc.html
367 lines (324 loc) · 32 KB
/
toc.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
<!DOCTYPE html>
<html>
<head>
<!-- always use https, not http -->
<script type="text/javascript" src='https://ajax.googleapis.com/ajax/libs/jquery/1.10.1/jquery.min.js'></script>
<link rel="stylesheet" href="html/CSS/home.css">
<!-- need this for navigation menu -->
<link rel="stylesheet" href="html/CSS/navbar.css">
<!-- subscription -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css">
<link rel="stylesheet" href="html/CSS/subscription.css">
</head>
<body>
<!-- Navigation menu -->
<div class ="navmenu">
<ul>
<li class="menu-item active"><a href="toc.html"><span class="menu-item">HOME</span></a></li>
<!-- <li class="menu-item"><a href="html/pages/presentations.html"><span class="menu-item">PRESENTATIONS</span></a></li>
<li class="menu-item"><a href="html/pages/gallery.html"><span class="menu-item">GALLERY</span></a></li> -->
<li class="menu-item"><a href="html/pages/about_new.html"><span class="menu-item">ABOUT</span></a></li>
</ul>
</div>
<!-- still image background
<div class="bg">
</div>
-->
<video autoplay muted loop id="myVideo" src="html/vids/background_v1.mov"></video>
<script>
var vid = document.getElementById("myVideo");
vid.playbackRate = 1.5;
</script>
<!-- <p id="title">Machine Learning Refined</p> -->
<br/><br/>
<p class="sub-title"> This is a blog about machine learning and deep learning fundamentals built by the authors of the
textbook <a class="redlink" target="_blank" href="http://mlrefined.com">Machine Learning Refined</a>
published by Cambridge University Press. The posts, cut into short series, use careful writing and interactive coding widgets
to provide an intuitive and playful way to learn about core concepts in AI - from some of the most basic to the most advanced.
<br/><br/>
Each and every post here is a Python Jupyter notebook, prettied up for the web, that you can download and run on your own machine
by pulling our <a class="redlink" target="_blank" href="https://github.com/jermwatt/mlrefined">GitHub repo</a>.
</p>
<br/><br/>
<hr style="display:block; margin-top: 0.5em; margin-bottom: 0.5em; margin-left: 240px; margin-right: 240px; border-style: inset; border-width: 1px;"/>
<br/>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter01" checked />
<label for="chapter01"><span>CHAPTER 1. Introduction to machine learning, deep learning, and reinforcement </span></label>
<div class="three_col">
<div class="description">
<p><strong>1.1. </strong> Machine learning, a framework for learning from data <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a> </p>
<p><strong>1.2. </strong> Introduction to problem types in machine learning coupled with overview of modern applications <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>1.3. </strong> The basic building blocks of machine learning and how they all fit together <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter02" checked/>
<label for="chapter02"><span>CHAPTER 2. Computational linear algebra and statistics</span></label>
<div class="four_col">
<div class="description">
<p><strong>2.1. </strong> Vectors and matrices, vector and matrix norms, linear functions and matrices <a target="_blank" class="sublink-active" href="html/pages/topics/Computational_Linear_Algebra.html m"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.2. </strong> Eigenvalues, eigenvectors and the power method <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.3. </strong> Fundamentals of probability and statistics, discrete and continuous distributions <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.4. </strong> Maximum likelihood approach and Bayes’ rule <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter03" checked/>
<label for="chapter03"><span>CHAPTER 3. Computational calculus</span></label>
<div class="four_col">
<div class="description">
<p><strong>3.1. </strong> The derivative and computation graphs <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.2. </strong> Automatic differentiation part 1: the forward method <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.3. </strong> Taylor series approximation <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.4. </strong> Vector valued derivatives <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter04" checked/>
<label for="chapter04"><span>CHAPTER 4. Mathematical Optimization I: Gradient descent</span></label>
<div class="four_col">
<div class="description">
<p><strong>4.1. </strong> Unconstrained optimality conditions <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.2. </strong> Naive and local search methods <a target="_blank" class="sublink-active" href="blog_posts/Mathematical_Optimization/Part_1_motivation_random_search.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.3. </strong> Gradient descent <a target="_blank" class="sublink-active" href="blog_posts/Mathematical_Optimization/Part_2_gradient_descent.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.4. </strong> Step-length rules, backtracking line search <a target="_blank" class="sublink-active" href="blog_posts/Mathematical_Optimization/Part_3_conservative_steplength_rules.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter05" checked/>
<label for="chapter05"><span>CHAPTER 5. Linear regression </span></label>
<div class="four_col">
<div class="description">
<p><strong>5.1. </strong> Linear regression: Least Squares and Least Absolute Deviations <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_1_Linear_regression.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.2. </strong> The probabilistic perspective on linear regression <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_1_Linear_regression.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.3. </strong> Performance metrics for regression <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.4. </strong> Feature selection and regularization <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter06" checked/>
<label for="chapter06"><span>CHAPTER 6. Linear two-class classification </span></label>
<div class="seven_col">
<div class="description">
<p><strong>6.1. </strong> From linear to logistic regression <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_2_logistic_regression.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>6.2. </strong> Logistic regression: geometric and probabilistic perspectives <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_2_logistic_regression.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>6.3. </strong> The classic perceptron and support vector machines <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_3_Perceptron.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>6.4. </strong> A unified view of two-class classification <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_4_support_vector_machines.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>6.5. </strong> Performance metrics for classification <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>6.6. </strong> Principles of feature engineering <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>6.7. </strong> General data pre-processing techniques <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter07" checked/>
<label for="chapter07"><span>CHAPTER 7. Linear multiclass classification</span></label>
<div class="four_col">
<div class="description">
<p><strong>7.1. </strong> One-versus-All classification <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_5_One_versus_all.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>7.2. </strong> Multiclass softmax classification: geometric and probabilistic perspectives <a target="_blank" class="sublink-active" href="blog_posts/Linear_Supervised_Learning/Part_6_multiclass_classification.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>7.3. </strong> A unified view of multiclass classification <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>7.4. </strong> Performance metrics <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter08" checked/>
<label for="chapter08"><span>CHAPTER 8. Mathematical Optimization II: Algorithms for dealing with large datasets</span></label>
<div class="five_col">
<div class="description">
<p><strong>8.1. </strong> Feature scaling and the “long narrow valley” problem with gradient descent <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.2. </strong> Scaling with data, stochastic gradient descent <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.3. </strong> Newton’s method and Quasi-Newton methods <a target="_blank" class="sublink-active" href="blog_posts/Mathematical_Optimization/Part_5_Newtons_method.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.4. </strong> Coordinate descent methods <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.5. </strong> Projections and projection methods <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter09" checked/>
<label for="chapter09"><span>CHAPTER 9. Linear dimension reduction techniques</span></label>
<div class="five_col">
<div class="description">
<p><strong>9.1. </strong> Linear representations of data <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>9.2. </strong> Principal component analysis (PCA): geometric and probabilistic perspectives <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>9.3. </strong> K-means clustering <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>9.4. </strong> Recommender systems <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>9.5. </strong> The general matrix factorization framework <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter10" checked/>
<label for="chapter10"><span>CHAPTER 10. Introduction to nonlinear learning</span></label>
<div class="seven_col">
<div class="description">
<p><strong>10.1. </strong> The search for natural laws <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>10.2. </strong> Geometric feature design done ‘by eye’ <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>10.3. </strong> Introduction to tools of the trade: kernel, neural network, and tree bases <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>10.4. </strong> The ideal scenario versus reality <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>10.5. </strong> Fixed versus adjustable basis functions and approximation <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>10.6. </strong> The gist of cross-validation <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>10.7. </strong> Which basis works best? <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter11" checked/>
<label for="chapter11"><span>CHAPTER 11. Kernel methods</span></label>
<div class="six_col">
<div class="description">
<p><strong>11.1. </strong> Motivation and basic examples <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>11.2. </strong> Failure to scale in the dimension of features <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>11.3. </strong> The kernel trick via the fundamental theorem of Linear algebra <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>11.4. </strong> Kernelizing supervised and unsupervised problems <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>11.5. </strong> Kernels as similarity matrices <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>11.6. </strong> The failure to scale with large datasets <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter12" checked/>
<label for="chapter12"><span>CHAPTER 12. Tree-based methods</span></label>
<div class="five_col">
<div class="description">
<p><strong>12.1. </strong> Recursively defined tree-based functions <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>12.2. </strong> Random forests <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>12.3. </strong> Greedy coordinate descent and the generic booster <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>12.4. </strong> Gradient boosting <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>12.5. </strong> Adaboost and logitboost <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter13" checked/>
<label for="chapter13"><span>CHAPTER 13. Multilayer perceptrons</span></label>
<div class="six_col">
<div class="description">
<p><strong>13.1. </strong> Computation graphs and the construction of endlessly complex functions <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.2. </strong> Automatic differentiation part 2: the backward method (a.k.a. Backpropagation) <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.3. </strong> Designing generic deep networks recursively <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.4. </strong> Computation graphs, deep networks, and efficient computation <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.5. </strong> PCA and the autoencoder <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.6. </strong> Cross-validation by regularization, early stopping, and dropout <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter14" checked/>
<label for="chapter14"><span>CHAPTER 14. Mathematical Optimization III: Optimization tricks for multilayer perceptrons</span></label>
<div class="seven_col">
<div class="description">
<p><strong>14.1. </strong> Nonlinear features, feature scaling, and batch-normalization <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.2. </strong> Regularization and convexification <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.3. </strong> Momentum and gradient descent <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.4. </strong> Normalized gradient descent and non-convex functions <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.5. </strong> General steepest descent methods <a target="_blank" class="sublink-active" href="blog_posts/Mathematical_Optimization/Part_4_general_steepest_descent.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.6. </strong> Stochastic and minibatch methods <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.7. </strong> Engineered first order methods for feedforward networks <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter15" checked/>
<label for="chapter15"><span>CHAPTER 15. Recurrent Neural Networks (RNNs)</span></label>
<div class="seven_col">
<div class="description">
<p><strong>15.1. </strong> The ‘knowledge versus data’ trade-off curve <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.2. </strong> Common examples of ordered data used in supervised learning <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.3. </strong> Recursive sequences and functions, Markov models <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.4. </strong> Basic and hidden recursive models <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.5. </strong> The simple Recurrent Neural Network <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.6. </strong> Learning long term dependencies, deficiencies of the simple RNN <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.7. </strong> Architectures for learning long term dependencies: the identity RNN, LSTM, and GRU <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter16" checked/>
<label for="chapter16"><span>CHAPTER 16. Convolutional Neural Networks (CNNs) - Part I</span></label>
<div class="five_col">
<div class="description">
<p><strong>16.1. </strong> Spatially ordered data, images, and convolutions <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>16.2. </strong> Convolutions and their many applications to signal and image processing <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>16.3. </strong> Histogram Features for real data: convolution and pooling operations <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>16.4. </strong> Learning with fixed convolution kernels <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>16.5. </strong> Learnable kernels and convolutional networks <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter17" checked/>
<label for="chapter17"><span>CHAPTER 17. Convolutional Neural Networks (CNNs) - Part II </span></label>
<div class="five_col">
<div class="description">
<p><strong>17.1. </strong> Classic and modern convolutional architectures <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>17.2. </strong> Structured output regression and localization <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>17.3. </strong> Transfer learning <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>17.4. </strong> Unsupervised learning pre-training <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>17.5. </strong> Adversarial examples and the fragility of convolutional networks <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter18" checked/>
<label for="chapter18"><span>CHAPTER 18. Introduction to Reinforcement Learning </span></label>
<div class="four_col">
<div class="description">
<p><strong>18.1. </strong> Fundamental ideas and examples <a target="_blank" class="sublink-active" href="blog_posts/Reinforcement_Learning/Fundamentals_of_reinforcement_learning.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>18.2. </strong> The basic Q-Learning algorithm <a target="_blank" class="sublink-active" href="blog_posts/Reinforcement_Learning/Q_learning.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>18.3. </strong> Exploration-exploitation trade-off, short-term long-term reward <a target="_blank" class="sublink-active" href="blog_posts/Reinforcement_Learning/Q_learning_enhancements.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>18.4. </strong> Generalizability of Q-Learning <a target="_blank" class="sublink-active" href="blog_posts/Reinforcement_Learning/On_generalizability_of_reinforcement_learning.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter19" checked/>
<label for="chapter19"><span>CHAPTER 19. Reinforcement Learning in large state spaces</span></label>
<div class="four_col">
<div class="description">
<p><strong>19.1. </strong> Challenges in scaling to large state spaces <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>19.2. </strong> Function approximators, Deep Q-Learning, and memory replay <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>19.3. </strong> Policy gradient method: geometric and probabilistic perspectives <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>19.4. </strong> Model based reinforcement and optimal control <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter20" checked/>
<label for="chapter20"><span>CHAPTER 20. Reinforcement Learning in large action spaces </span></label>
<div class="four_col">
<div class="description">
<p><strong>20.1. </strong> The limits of discretization <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>20.2. </strong> Deep Q-Learning in the continuous action domain <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>20.3. </strong> Actor-Critic methods <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>20.4. </strong> Approximate Q-Learning and the wire-fitting algorithm <a target="_blank" class="sublink-inactive" href=""> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
<br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/>
<!-- uncomment subscription
<!- - subscription button - ->
<form action="https://formspree.io/[email protected]" method="POST">
<input type="email" name="email" placeholder="Enter your email to get notified when new posts are published" onfocus="this.placeholder=''" onblur="this.placeholder='Enter your email to get notified when new posts are published'" autocomplete="off">
<button type="submit" value="Send">Subscribe <i class="fa fa-envelope-o"></i></button>
</form>
-->
<br/><br/><br/><br/><br/><br/><br/><br/><br/><br/>
<script>
$(document).ready(function(){
$('.hover').hover(function(){
$(this).addClass('flip');
},function(){
$(this).removeClass('flip');
});
});
</script>
</body>
</html>