1/ A ∞-wide NN of *any architecture* is a Gaussian process (GP) at init. The NN in fact evolves linearly in function space under SGD, so is a GP at *any time* during training. https://t.co/v1b6kndqCk With Tensor Programs, we can calculate this time-evolving GP w/o trainin any NN
https://t.co/6RO7VZDQNZ
https://t.co/OOoOMdPOsR
More from Data science
I have always emphasized on the importance of mathematics in machine learning.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
📘 Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
📘 Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
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1/x Fort Detrick History
Mr. Patrick, one of the chief scientists at the Army Biological Warfare Laboratories at Fort Detrick in Frederick, Md., held five classified US patents for the process of weaponizing anthrax.
2/x
Under Mr. Patrick’s direction, scientists at Fort Detrick developed a tularemia agent that, if disseminated by airplane, could cause casualties & sickness over 1000s mi². In a 10,000 mi² range, it had 90% casualty rate & 50% fatality rate
3/x His team explored Q fever, plague, & Venezuelan equine encephalitis, testing more than 20 anthrax strains to discern most lethal variety. Fort Detrick scientists used aerosol spray systems inside fountain pens, walking sticks, light bulbs, & even in 1953 Mercury exhaust pipes
4/x After retiring in 1986, Mr. Patrick remained one of the world’s foremost specialists on biological warfare & was a consultant to the CIA, FBI, & US military. He debriefed Soviet defector Ken Alibek, the deputy chief of the Soviet biowarfare program
https://t.co/sHqSaTSqtB
5/x Back in Time
In 1949 the Army created a small team of chemists at "Camp Detrick" called Special Operations Division. Its assignment was to find military uses for toxic bacteria. The coercive use of toxins was a new field, which fascinated Allen Dulles, later head of the CIA
Mr. Patrick, one of the chief scientists at the Army Biological Warfare Laboratories at Fort Detrick in Frederick, Md., held five classified US patents for the process of weaponizing anthrax.
2/x
Under Mr. Patrick’s direction, scientists at Fort Detrick developed a tularemia agent that, if disseminated by airplane, could cause casualties & sickness over 1000s mi². In a 10,000 mi² range, it had 90% casualty rate & 50% fatality rate
3/x His team explored Q fever, plague, & Venezuelan equine encephalitis, testing more than 20 anthrax strains to discern most lethal variety. Fort Detrick scientists used aerosol spray systems inside fountain pens, walking sticks, light bulbs, & even in 1953 Mercury exhaust pipes
4/x After retiring in 1986, Mr. Patrick remained one of the world’s foremost specialists on biological warfare & was a consultant to the CIA, FBI, & US military. He debriefed Soviet defector Ken Alibek, the deputy chief of the Soviet biowarfare program
https://t.co/sHqSaTSqtB
5/x Back in Time
In 1949 the Army created a small team of chemists at "Camp Detrick" called Special Operations Division. Its assignment was to find military uses for toxic bacteria. The coercive use of toxins was a new field, which fascinated Allen Dulles, later head of the CIA