30:15
The Math of "The Trillion Dollar Equation"
61K views • 2 months ago
1:12:26
[Lecture] Monte Carlo evaluation and control: A Gridworld Example | Intro to Markov Chains and RL
469 views • 2 months ago
1:05:42
[Lecture] Is it safe to differentiate under the integral? Lebesgue Dominated Convergence theorem
341 views • 2 months ago
1:02:19
[ Lecture ] Intro to Monte Carlo methods in Reinforcement Learning | Intro to Markov Chains and RL
110 views • 2 months ago
1:14:15
[ Lecture ] Almost Everywhere vs L1 convergence and an absolute summability theorem | Intro Analysis
72 views • 2 months ago
1:06:16
[ Lecture ] L1 is complete and the monotone convergence theorem for integrals | Intro to Analysis
48 views • 2 months ago
1:21:02
L1 vs "L"1, Null sets & functions, Almost Everywhere vs Norm Convergence | Intro to Analysis
51 views • 2 months ago
1:19:24
Live coding the Gambler's Problem using Value Iteration | Intro to Markov Chains and Reinforcement L
178 views • 2 months ago
1:11:13
Lebesgue Integrals 3: Absolute value of functions and series | Intro to Functional Analysis
31 views • 2 months ago
1:19:00
The Bellman Equation and 1 Player PIG solved with Value Iteration | Intro to Markov Chains and RL
117 views • 2 months ago
15:22
How far does a simple random walk go in n steps? E|X_n| = ?
363 views • 2 months ago
1:09:39
Lebesgue Integral 2: Write the function as an infinite sum of step functions | Intro to Analysis
93 views • 3 months ago
42:41
Markov Chains with actions & dice game PIG | Intro to Markov Chains and Reinforcement Learning
134 views • 3 months ago
1:17:12
Lebesgue Integral 1: Step functions & Interval Countable Additivity | Intro to Functional Analysis
75 views • 3 months ago
1:21:36
Cauchy Sequences, Complete and Banach Spaces | Intro to Functional Analysis
108 views • 3 months ago
1:19:16
Creating Markov chains by enlarging the state space & Baby Bellman Eqn | Intro Markov Chains and RL
123 views • 3 months ago
1:17:40
Closed/compact & closed ball is compact iff finite dimensional space | Intro to Functional Analysis
117 views • 3 months ago
1:12:15
Solving probabilities and expected values for Markov Chains & the (baby) Bellman Eqn | Intro to RL
331 views • 3 months ago
1:08:15
Pointwise vs L1 vs Linfinity convergence + Equivalence of norms on finite dimensional spaces | Lec 3
167 views • 3 months ago
1:16:01
Two state Markov chain example and the steady state distribution | Intro to Markov Chains Lecture 3
237 views • 3 months ago
1:13:43
Normed Vector Spaces and Function Spaces | Intro to Functional Analysis Lecture 2
160 views • 3 months ago
1:18:43
Snakes+Ladders probability problem in spreadsheet and Python | Intro to Markov Chains Lec 2
214 views • 3 months ago
1:16:23
Functions are just fancy vectors | Intro to Functional Analysis Lecture 1
589 views • 3 months ago
1:14:40
What is Reinforcement Learning? Lecture with 4 Examples | Intro to Markov Chains and RL
383 views • 3 months ago
21:05
The FAST trick to test if n is prime (with Python code) | AKS Primality Testing in poly(log n) time
725 views • 4 months ago
13:28
The Hidden Patterns of Pascal's Triangle (featuring Marc Evanstein / music.py)
1.6K views • 4 months ago
1:07:52
Intro to Data Science Lecture 22 | letter2Vec (baby names version of word2vec)
103 views • 5 months ago
1:13:01
Intro to Data Science Lecture 21 | MNIST Neural net Regularization, autoencoders, word2vec overview
193 views • 5 months ago
1:08:31
Intro to Data Science Lecture 20 | MNIST in JAX: softmax, cross entropy loss, Multilayer perceptron
124 views • 5 months ago
1:13:53
Intro to Data Science Lecture 19 | MNIST with JAX package, from linear regression to neural networks
123 views • 5 months ago
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