forked from ritchieng/deep-learning-wizard
-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmkdocs.yml
executable file
·221 lines (204 loc) · 9.87 KB
/
mkdocs.yml
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
# Project information
site_name: 'Deep Learning Wizard'
site_description: 'We try to make learning deep learning, deep bayesian learning, and deep reinforcement learning math and code easier. Used by thousands.'
site_author: 'Ritchie Ng'
site_url: 'https://www.deeplearningwizard.com/'
# Navigation
nav:
- Home: index.md
- About:
- Intro: home.md
- About Us: about.md
- Reviews: review.md
- AI Pipeline: pipeline.md
- Consultancy: consultancy.md
- Deep Learning (CPU/GPU):
- Introduction: deep_learning/intro.md
- Course Progression: deep_learning/course_progression.md
- Matrices: deep_learning/practical_pytorch/pytorch_matrices.md
- Gradients: deep_learning/practical_pytorch/pytorch_gradients.md
- Linear Regression: deep_learning/practical_pytorch/pytorch_linear_regression.md
- Logistic Regression: deep_learning/practical_pytorch/pytorch_logistic_regression.md
- Feedforward Neural Networks (FNN): deep_learning/practical_pytorch/pytorch_feedforward_neuralnetwork.md
- Convolutional Neural Networks (CNN): deep_learning/practical_pytorch/pytorch_convolutional_neuralnetwork.md
- Recurrent Neural Networks (RNN): deep_learning/practical_pytorch/pytorch_recurrent_neuralnetwork.md
- Long Short Term Memory Neural Networks (LSTM): deep_learning/practical_pytorch/pytorch_lstm_neuralnetwork.md
- Autoencoders (AE): deep_learning/practical_pytorch/pytorch_autoencoder.md
- Fully-connected Overcomplete Autoencoder (AE): deep_learning/practical_pytorch/pytorch_fc_overcomplete_ae.md
# - Fully-connected Undercomplete Autoencoder (AE): deep_learning/practical_pytorch/pytorch_fc_undercomplete_ae.md
# - Convolutional Overcomplete Variational Autoencoder (VAE): deep_learning/practical_pytorch/pytorch_autoencoder.md
# - Convolutional Overcomplete Adversarial Autoencoder (AAE): deep_learning/practical_pytorch/pytorch_autoencoder.md
# - Generative Adversarial Networks (GAN): deep_learning/practical_pytorch/pytorch_autoencoder.md
- Derivative, Gradient and Jacobian: deep_learning/boosting_models_pytorch/derivative_gradient_jacobian.md
- Forward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression): deep_learning/boosting_models_pytorch/forwardpropagation_backpropagation_gradientdescent.md
- From Scratch Logistic Regression Classification: deep_learning/fromscratch/fromscratch_logistic_regression.md
- From Scratch CNN Classification: deep_learning/fromscratch/fromscratch_cnn.md
# - From Scratch RNN Classification: deep_learning/fromscratch/fromscratch_rnn_classification.md
# - From Scratch LSTM Classification: deep_learning/fromscratch/fromscratch_lstm_classification.md
# - From Scratch AE: deep_learning/fromscratch/fromscratch_ae_classification.md
- Learning Rate Scheduling: deep_learning/boosting_models_pytorch/lr_scheduling.md
- Optimization Algorithms: deep_learning/boosting_models_pytorch/optimizers.md
- Weight Initialization and Activation Functions: deep_learning/boosting_models_pytorch/weight_initialization_activation_functions.md
- Supervised Learning to Reinforcement Learning (RL): deep_learning/deep_reinforcement_learning_pytorch/supervised_to_rl.md
- Markov Decision Processes (MDP) and Bellman Equations: deep_learning/deep_reinforcement_learning_pytorch/bellman_mdp.md
- Dynamic Programming: deep_learning/deep_reinforcement_learning_pytorch/dynamic_programming_frozenlake.md
- Speed Optimization Basics Numba: deep_learning/production_pytorch/speed_optimization_basics_numba.md
- Additional Readings: deep_learning/readings.md
- Machine Learning (CPU/GPU):
- Introduction: machine_learning/intro.md
- GPU DataFrames: machine_learning/gpu/rapids_cudf.md
- GPU/CPU Fractional Differencing: machine_learning/gpu/gpu_fractional_differencing.md
# - Linear Regression: machine_learning/gpu/rapids_cudf.md
# - Ridge Regression: machine_learning/gpu/rapids_cudf.md
# - Kalman Filter: machine_learning/gpu/rapids_cudf.md
# - Stochastic Gradient Descent: machine_learning/gpu/rapids_cudf.md
# - K-nearest Neighbours Classification: machine_learning/gpu/rapids_cudf.md
# - K-Means Clustering: machine_learning/gpu/rapids_cudf.md
# - Density-Based Spatial Clustering of Applications with Noise (DBSCAN): machine_learning/gpu/rapids_cudf.md
# - Singular Value Decomposition (SVD), Dimensionality Reduction: machine_learning/gpu/rapids_cudf.md
# - Principal Component Analysis (PCA), Dimensionality Reduction: machine_learning/gpu/rapids_cudf.md
# - Uniform Manifold Approximation and Projection (UMAP), Dimensionality Reduction: machine_learning/gpu/rapids_cudf.md
- Programming:
- Introduction: programming/intro.md
- C++: programming/cpp/cpp.md
- Bash: programming/bash/bash.md
- Python: programming/python/python.md
- R: programming/r/r.md
- Javascript: programming/javascript/javascript.md
- Electron: programming/electron/electron.md
- Sympy: programming/sympy/calculus_sympy.md
- NumPy and CuPy: programming/numpycupy/linalg_numpy_cupy.md
# - Matplotlib: programming/plotting/matplotlib.md
# - Plotting:
# - Introduction: programming/plotting/intro.md
# - Out of Distribution Data:
# - Introduction: ood/intro.md
- Database:
- Introduction: database/intro.md
- Cassandra Cluster Setup: database/setting_up_cluster.md
- News:
- Welcome: news/news.md
- Fractional Differencing with GPU (GFD), DBS and NVIDIA, September 2019: news/dbs_gpu_rapids_nvidia_ensemble_frac_diff.md
- Deep Learning Introduction, Defence and Science Technology Agency (DSTA) and NVIDIA, June 2019: news/defence_and_science_technology_agency_dsta_nvidia_talk_2016_06.md
- Oral Presentation for AI for Social Good Workshop ICML, June 2019: news/detect_waterbone_debris_ai_for_social_good_icml_2019_06.md
- IT Youth Leader of The Year 2019, March 2019: news/it_youth_leader_2019_03_11.md
- AMMI (AIMS) supported by Facebook and Google, November 2018: news/ammi_facebook_google_recap_2018_11_21.md
- NExT++ AI in Healthcare and Finance, Nanjing, November 2018: news/nanjing_next_nus_tsinghua_ai_finance_healthcare_2018_11_01.md
- Recap of Facebook PyTorch Developer Conference, San Francisco, September 2018: news/facebook_pytorch_devcon_recap_2018_10_02.md
- Facebook PyTorch Developer Conference, San Francisco, September 2018: news/facebook_pytorch_developer_conference_2018_09_05.md
- NUS-MIT-NUHS NVIDIA Image Recognition Workshop, Singapore, July 2018: news/nvidia_nus_mit_datathon_2018_07_05.md
- Featured on PyTorch Website 2018: news/deep_learning_wizard_1y_2018_06_01.md
- NVIDIA Self Driving Cars & Healthcare Talk, Singapore, June 2017: news/nvidia_self_driving_cars_talk_2017_06_21.md
- NVIDIA Inception Partner Status, Singapore, May 2017: news/deep_learning_wizard_nvidia_inception_2018_05_01.md
# Configuration
theme:
name: material
custom_dir: material
static_templates:
- 404.html
# Don't include MkDocs' JavaScript
include_search_page: false
search_index_only: true
# Default values, taken from mkdocs_theme.yml
language: en
features:
- content.code.annotate
- content.tabs.link
# - header.autohide
# - navigation.expand
# - navigation.indexes
# - navigation.instant
- navigation.sections
- navigation.tabs
# - navigation.tabs.sticky
- navigation.top
- navigation.tracking
- search.highlight
- search.share
- search.suggest
# - toc.integrate
palette:
- scheme: default
primary: indigo
accent: indigo
toggle:
icon: material/toggle-switch-off-outline
name: Switch to dark mode
- scheme: slate
primary: indigo
accent: indigo
toggle:
icon: material/toggle-switch
name: Switch to light mode
font:
text: Roboto
code: Roboto Mono
favicon: ./docs/assets/favicon.ico
icon:
logo: fontawesome/regular/chart-bar
# Extensions
markdown_extensions:
- admonition
- abbr
- attr_list
- def_list
- footnotes
- meta
- md_in_html
- toc:
permalink: true
- pymdownx.arithmatex:
generic: true
- pymdownx.betterem:
smart_enable: all
- pymdownx.caret
- pymdownx.details
- pymdownx.emoji:
emoji_index: !!python/name:materialx.emoji.twemoji
emoji_generator: !!python/name:materialx.emoji.to_svg
- pymdownx.highlight
- pymdownx.inlinehilite
- pymdownx.keys
- pymdownx.magiclink:
repo_url_shorthand: true
user: squidfunk
repo: mkdocs-material
- pymdownx.mark
- pymdownx.smartsymbols
- pymdownx.superfences:
custom_fences:
- name: mermaid
class: mermaid-experimental
format: !!python/name:pymdownx.superfences.fence_code_format
- pymdownx.tabbed
- pymdownx.tasklist:
custom_checkbox: true
- pymdownx.tilde
# Equations
extra_javascript:
- 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML'
# Social
extra:
analytics:
provider: google
property: 'UA-122083328-1'
social:
- icon: 'fontawesome/brands/youtube'
link: 'https://www.youtube.com/channel/UCJz2MIjiCosOQCwhnsYxeEw'
- icon: 'fontawesome/brands/twitter'
link: 'https://twitter.com/deeplearningwiz'
- icon: 'fontawesome/brands/facebook'
link: 'https://www.facebook.com/DeepLearningWizard/'
- icon: 'fontawesome/brands/linkedin'
link: 'https://www.linkedin.com/company/deeplearningwizard/'
- icon: 'fontawesome/brands/github'
link: 'https://github.com/ritchieng'
disqus: 'deep-learning-wizard'
# Search
plugins:
- search
# Source to repository
repo_name: ritchieng/deep-learning-wizard
repo_url: https://github.com/ritchieng/deep-learning-wizard
# Copyright
copyright: 'Copyright © 2021 Deep Learning Wizard by Ritchie Ng'