wildml.com

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IP Address 35.224.76.8

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WildML – Artificial Intelligence, Deep Learning, and NLP

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Keywords

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No	Meta Name	Value
1	viewport	width=device-width, initial-scale=1

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Type	Status
HTML 5
Responsive Website

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Text / Code Ratio 39.36 %

wildml.com has a website text/code ratio of 39.36 %. Search engine crawlers tend to not pick up pages with inadequate content.

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Titles

H11
H216
H327
H45
H50
H60

No	Text
1	Wildml

No	Text
1	Posts
2	Basics of market microstructure
3	Data
4	A few trading strategy metrics
5	Supervised learning
6	A typical strategy development workflow
7	Deep reinforcement learning for trading
8	The case for reinforcement learning
9	The case for trading agent research
10	Conclusion
11	Posts navigation
12	Subscribe to blog via email
13	Recent posts
14	Archives
15	Categories
16	Meta

No	Text
1	ıntroduction to learning to trade with reinforcement learning
2	Aı and deep learning in 2017 – a year in review
3	Reinforcement learning beats humans at their own games
4	Evolution algorithms make a comeback
5	Wavenets, cnns, and attention mechanisms
6	The year of deep learning frameworks
7	Learning resources
8	Applications: aı & medicine
9	Applications: art & gans
10	Applications: self-driving cars
11	Applications: cool research projects
12	Datasets
13	Deep learning, reproducibility, and alchemy
14	Artificial ıntelligence made in canada and china
15	Hardware wars: nvidia, ıntel, google, tesla
16	Hype and failures
17	High-profile hires and departures
18	Startup ınvestments and acquisitions
19	And finally, happy new year! thanks for sticking with this post for so long :)
20	Hype or not? some perspective on openaı’s dota 2 bot
21	Learning reinforcement learning (with code, exercises and solutions)
22	Rnns in tensorflow, a practical guide and undoc***ented features
23	Deep learning for chatbots, part 2 – ımplementing a retrieval-based model in tensorflow
24	Deep learning for chatbots, part 1 – ıntroduction
25	Attention and memory in deep learning and nlp
26	ımplementing a cnn for text cla***ification in tensorflow
27	Understanding convolutional neural networks for nlp

No	Text
1	Why study reinforcement learning
2	How to study reinforcement learning
3	Table of contents
4	List of ımplemented algorithms
5	Retrieval-based bots

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1	Price chart (Middle)
2	Trade History (Right)
3	Order Book (Left)
4	Trade
5	BookUpdate
6	BookSnapshot
7	Net PnL (Net Profit and Loss)
8	Alpha and Beta
9	Sharpe Ratio
10	Data Analysis
11	Supervised Model Training:
12	Policy Development
13	Strategy Backtesting:
14	Parameter Optimization:
15	Simulation & Paper Trading:
16	Live Trading:
17	Agent
18	Environment
19	State
20	Time Scale
21	Action Space
22	Reward Function
23	Trained directly in Simulation Environments
24	Learning to adapt to market conditions
25	Ability to model other agents
26	Live Testing and Fast Iteration Cycle
27	Large Multiplayer Environments
28	Learning to Exploit other Agents & Manip****te the Market
29	Spa**** Rewards & Exploration
30	Transfer Learning and Auxiliary Tasks
31	Update (August 17th, 2017)
32	1v1 is not comparable to 5v5
33	Bots have access to more information
34	Reaction Times
35	Learning to play a single specific character
36	Hard-coded restrictions
37	significantly easier than beating a human champion in the game of Go
38	We did not make sudden progress in AI because our algorithms are so smart – it worked because our researchers are smart about setting up the problem in just the right way to work around the limitations of current techniques
39	Trained entirely through self-play
40	A major step for AI + eSports
41	Partially Observable environments
42	Skip all the talk and go directly to the Github Repo with code and exercises.
43	The Code and data for this tutorial is on Github.
44	The full code is available on Github.

No	Text
1	Maximum Drawdown
2	Value at Risk (VaR)
3	End-to-End Optimization of what we care about
4	Learned Policies
5	Multi-Agent Self-Play
6	Continuous Time
7	Nonstationary, Lifelong Learning, and Catastrophic Forgetting

No	Text
1	Thanks a lot to @aerinykim, @suzatweet and @hardmaru for the useful feedback!
2	liquidity
3	illiquid
4	taker
5	maker
6	Asks
7	Bids
8	best ask (2)
9	best bid (2)
10	spread
11	level
12	consumed
13	market
14	limit
15	market order
16	right now
17	limit order
18	take liquidity
19	market takers
20	market makers
21	providing liquidity
22	mid price
23	policy
24	rule-based policy
25	Realized PnL
26	closes
27	net profit
28	Unrealized PnL
29	directly
30	That’s a very powerful concept. By building an increasingly complex simulation environment that models the real world you can train very sophisticated agents that learn to take environment constraints into account.
31	when
32	generative

LINK ANALYSIS

Total Link Count: 305

Internal Link Count : 79

No	Text	Type
1	WildML	text
2	Home	text
3	Deep Learning Glossary	text
4	Contact	text
5	About	text
6	February 10, 2018February 10, 2018	text
7	Introduction to Learning to Trade with Reinforcement Learning	text
8	-	image
9	-	image
10	December 31, 2017May 1, 2020	text
11	AI and Deep Learning in 2017 – A Year in Review	text
12	-	image
13	-	image
14	-	image
15	August 12, 2017August 16, 2017	text
16	Hype or Not? Some Perspective on OpenAI’s DotA 2 Bot	text
17	See the Hacker News Discussion for additional context.	text
18	October 2, 2016June 11, 2017	text
19	Learning Reinforcement Learning (with Code, Exercises and Solutions)	text
20	August 21, 2016August 29, 2016	text
21	RNNs in Tensorflow, a Practical Guide and Undoc***ented Features	text
22	previous tutorial series	text
23	Continue reading “RNNs in Tensorflow, a Practical Guide and Undoc***ented Features”	text
24	July 4, 2016August 18, 2016	text
25	Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow	text
26	Continue reading “Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow”	text
27	April 6, 2016May 30, 2016	text
28	Deep Learning for Chatbots, Part 1 – Introduction	text
29	Continue reading “Deep Learning for Chatbots, Part 1 – Introduction”	text
30	January 3, 2016April 27, 2016	text
31	Attention and Memory in Deep Learning and NLP	text
32	Continue reading “Attention and Memory in Deep Learning and NLP”	text
33	December 11, 2015February 4, 2016	text
34	Implementing a CNN for Text Cla***ification in TensorFlow	text
35	Continue reading “Implementing a CNN for Text Cla***ification in TensorFlow”	text
36	November 7, 2015January 10, 2016	text
37	Understanding Convolutional Neural Networks for NLP	text
38	Continue reading “Understanding Convolutional Neural Networks for NLP”	text
39	Page 2	text
40	Next page	text
41	Introduction to Learning to Trade with Reinforcement Learning	text
42	AI and Deep Learning in 2017 – A Year in Review	text
43	Hype or Not? Some Perspective on OpenAI’s DotA 2 Bot	text
44	Learning Reinforcement Learning (with Code, Exercises and Solutions)	text
45	RNNs in Tensorflow, a Practical Guide and Undoc***ented Features	text
46	Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow	text
47	Deep Learning for Chatbots, Part 1 – Introduction	text
48	Attention and Memory in Deep Learning and NLP	text
49	February 2018	text
50	December 2017	text

External Link Count : 226

No	Text	Type
1	AI Newsletter	text
2	@aerinykim	text
3	@suzatweet	text
4	@hardmaru	text
5	dozens of exchanges	text
6	this page	text
7	candlestick chart	text
8	API Doc***entation	text
9	Alpha	text
10	Beta	text
11	Sharpe Ratio	text
12	Maximum Drawdown	text
13	Value at Risk	text
14	Sharpe Ratio	text
15	MACD	text
16	Sharpe Ratio	text
17	Maximum Drawdown	text
18	auxiliary tasks	text
19	WildML newsletter	text
20	AlphaGo	text
21	Nature paper	text
22	AlphaGo Zero	text
23	Nature Paper	text
24	Thinking Fast and Slow with Deep Learning and Tree Search	text
25	AlphaZero	text
26	AlphaGo Teach	text
27	Libratus	text
28	Science paper	text
29	DeepStack	text
30	research environment	text
31	initial success in 1v1 Dota 2	text
32	demonstrated	text
33	a blog post and a set of five research papers	text
34	https://eng.uber.com/wp-content/uploads/2017/12/frostbite_ga_noframeskip.mp4	text
35	Tacotron 2 text-to-speech system	text
36	audio samples	text
37	WaveNet	text
38	ma***ive speed improvements	text
39	Attention is All you Need	text
40	PyTorch	text
41	Chainer	text
42	Tensorflow 1.0	text
43	Tensorflow Fold	text
44	Tensorflow Transform	text
45	DeepMind’s higher-level Sonnet library	text
46	eager execution	text
47	CoreML	text
48	Pyro	text
49	Gluon	text
50	Michelangelo	text
51	ONNX	text
52	OpenAI Roboschool	text
53	OpenAI Baselines	text
54	Tensorflow Agents	text
55	Unity ML Agents	text
56	Nervana Coach	text
57	Facebook’s ELF	text
58	DeepMind Pycolab	text
59	Geek.ai MAgent	text
60	Google’s deeplearn.js	text
61	MIL WebDNN	text
62	Theano	text
63	announcement	text
64	Deep RL Bootcamp	text
65	Convolutional Neural Networks for Visual Recognition	text
66	course website	text
67	Natural Language Processing with Deep Learning	text
68	course website	text
69	Theories of Deep Learning	text
70	Coursera Deep Learning specialization	text
71	Deep Learning and Reinforcement Summer School in Montreal	text
72	Deep Reinforcement Learning Fall 2017 course	text
73	Tensorflow Dev Summit	text
74	NIPS 2017	text
75	ICLR 2017	text
76	EMNLP 2017	text
77	Deep Reinforcement Learning: An Overview	text
78	A Brief Introduction to Machine Learning for Engineers	text
79	Neural Machine Translation	text
80	Neural Machine Translation and Sequence-to-sequence Models: A Tutorial	text
81	The End of Human Doctors blog post series	text
82	a Deep learning algorithm that does as well as dermatologists in identifying skin cancer	text
83	Nature article here	text
84	developed a model which can diagnose irregular heart rhythms, also known as arrhythmias, from single-lead ECG signals better than a cardiologist	text
85	was full of “inexcusable” mistakes	text
86	chest x-ray dataset	text
87	upon closer inspection	text
88	Machine Learning for Creativity and Design	text
89	Google’s QuickDraw	text
90	finish your drawings for you	text
91	CycleGAN	text
92	DiscoGAN	text
93	StarGAN	text
94	pix2pixHD	text
95	missed several red lights	text
96	details about its car visualization platform	text
97	hit 2 million miles	text
98	got their first real riders in April	text
99	completely took out the human operators in Phoenix, Arizona	text
100	testing and simulation technology	text
101	building its own autonomous driving hard- and software	text
102	now underway	text
103	seen much of an update	text
104	confirmed	text
105	published	text
106	Background removal with Deep Learning	text
107	Creating Anime characters with Deep Learning	text
108	Colorizing B&W Photos with Neural Networks	text
109	Mario Kart (SNES) played by a neural network	text
110	A Real-time Mario Kart 64 AI	text
111	Spotting Forgeries using Deep Learning	text
112	Edges to Cats	text
113	The Unsupervised Sentiment Neuron	text
114	Learning to Communicate	text
115	The Case for Learning Index Structures	text
116	Attention is All You Need	text
117	Mask R-CNN	text
118	Deep Image Prior for denoising, superresolution, and inpainting	text
119	Youtube Bounding Boxes	text
120	Google QuickDraw Data	text
121	DeepMind Open Source Datasets	text
122	Google Speech Commands Dataset	text
123	Atomic Visual Actions	text
124	Several updates to the Open Images data set	text
125	Nsynth dataset of annotated musical notes	text
126	Quora Question Pairs	text
127	Reinforcement Learning That Matters	text
128	Are GANs Created Equal? A Large-Scale Study	text
129	On the State of the Art of Evaluation in Neural Language Models	text
130	compared recent Deep Learning approaches to Alchemy	text
131	responded	text
132	new office in Toronto	text
133	Edmonton, Canada	text
134	expanding to Montreal	text
135	competing	text
136	new lab in Beijing	text
137	t**an V	text
138	are now available on its cloud platform	text
139	unveiled a new set of chips	text
140	working on its own AI hardware	text
141	come from China	text
142	everyone was hating on IBM Watson	text
143	repeated failures in healthcare	text
144	natural language generation paper	text
145	Machine Learning for markets	text
146	left Baidu	text
147	raised	text
148	landing.ai	text
149	Gary Marcus stepped down as the director of Uber’s artificial intelligence lab	text
150	hired away Siri’s Natural Language Understanding Chief	text
151	left OpenAI to start a new robotics company	text
152	continued	text
153	Microsoft acquired deep learning startup Maluuba	text
154	Google Cloud acquired Kaggle	text
155	Softbank bought robot maker Boston Dynamics	text
156	Facebook bought AI a***istant startup Ozlo	text
157	Samsung acquired Fluently to build out Bixby	text
158	Mythic raised $8.8 million to put AI on a chip	text
159	Element AI, a platform for companies to build AI solutions, raised $102M	text
160	Drive.ai raised $50M and added Andrew Ng to its board	text
161	Graphcore raised $30M	text
162	Appier raised a $33M Series C	text
163	Prowler.io raised $13M	text
164	Sophia Genetics raises $30 million to help doctors diagnose using AI and genomic data	text
165	published a blog post	text
166	this tweetstorm by @smerity	text
167	OpenAI’s DotA 2 bot	text
168	The International	text
169	released	text
170	August 12, 2017	text
171	August 12, 2017	text
172	AlphaGo	text
173	game’s bot API	text
174	were hardcoded	text
175	here	text
176	here	text
177	confirmed	text
178	@smerity	text
179	Skip all the talk and go directly to the Github Repo with code and exercises.	text
180	learning to play Atari Games from raw pixels	text
181	Mastering the Game of Go	text
182	David Silver’s Reinforcement Learning Course	text
183	Richard Sutton’s & Andrew Barto’s Reinforcement Learning: An Introduction (2nd Edition)	text
184	Github repository	text
185	OpenAI Gym	text
186	All of this is in the Github repository	text
187	Introduction to RL problems, OpenAI gym	text
188	MDPs and Bellman Equations	text
189	Dynamic Programming: Model-Based RL, Policy Iteration and Value Iteration	text
190	Monte Carlo Model-Free Prediction & Control	text
191	Temporal Difference Model-Free Prediction & Control	text
192	Function Approximation	text
193	Deep Q Learning	text
194	Policy Gradient Methods	text
195	Dynamic Programming Policy Evaluation	text
196	Dynamic Programming Policy Iteration	text
197	Dynamic Programming Value Iteration	text
198	Monte Carlo Prediction	text
199	Monte Carlo Control with Epsilon-Greedy Policies	text
200	Monte Carlo Off-Policy Control with Importance Sampling	text
201	SARSA (On Policy TD Learning)	text
202	Q-Learning (Off Policy TD Learning)	text
203	Q-Learning with Linear Function Approximation	text
204	Deep Q-Learning for Atari Games	text
205	Double Deep-Q Learning for Atari Games	text
206	Policy Gradient: REINFORCE with Baseline	text
207	Policy Gradient: Actor Critic with Baseline	text
208	Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces	text
209	Using tf.SequenceExample	text
210	Batching and Padding	text
211	Dynamic RNN	text
212	Bidirectional Dynamic RNN	text
213	RNN Cells and Cell Wrappers	text
214	Masking the Loss	text
215	The Code and data for this tutorial is on Github.	text
216	big bets	text
217	Operator	text
218	x.ai	text
219	Chatfuel	text
220	Howdy’s Botkit	text
221	bot developer framework	text
222	interview	text
223	The full code is available on Github.	text
224	Convolutional Neural Networks for Sentence Cla***ification	text
225	WordPress.org	text
226	Proudly powered by WordPress	text