00:14:26 Reshmi Ghosh (TA): Mute yourselves when you join!
00:18:53 Anon. Threshold: Best local linear approimation
00:19:13 Anon. Hodgkin-Huxley: gradient of function
00:19:13 Anon. Variance: The change in x with respect to the change in y
00:19:14 Anon. Sample: Rate of change of a function
00:19:16 Anon. Sigmoid: slope of a function
00:19:18 Anon. ICA: Rate of change of a variable with respect to another
00:19:28 Anon. YOLOv2: the change of a function dependent on change of inputs
00:19:36 Anon. DFS: a s.t. delta y = a * delta x
00:23:15 Anon. Variance: Alpha will be a vector
00:23:17 Anon. Algorithm: derivative would be a vector
00:23:47 Anon. Voltage-gate: gradient
00:23:47 Anon. Algorithm: derivative in all directions of the input
00:23:57 Anon. Variance: You need one output
00:24:03 Anon. Voltage-gate: column vector
00:24:05 Anon. Variance: So you need to do a dot product
00:24:20 Anon. LR: column vector
00:24:52 Anon. YOLOv3: is f a vector in this question?
00:25:01 Anon. YOLOv2: derivative is a vector with each component for each input. derivative is transpose of f(x) if vector
00:25:21 Jinhyung David Park (TA): @Nicky just a scalar
00:25:24 Anon. Supervised: Nx1
00:25:29 Anon. Markov Chain: n*1
00:25:51 Anon. YOLOv3: the derivative must be a row vector
00:26:11 Anon. Whyami: 1x N
00:26:14 Anon. Supervised: 1*N
00:26:16 Anon. LR: 1XN
00:29:07 Anon. Layer: alpha1*delx1
00:30:04 Anon. YOLOv3: just to be clear, if f is not a scalar and is instead a vector, then the shape of alpha would no longer be a 1xn correct?
00:31:07 Anon. YOLOv3: for example if f was 2x1 alpha would be 2xn
00:33:29 Anxiang Zhang (TA): 7 seconds
00:34:37 Anon. Autograd: I wasn't able to see the pole. Why was that ?
00:34:46 Reshmi Ghosh (TA): That is weird
00:35:20 Reshmi Ghosh (TA): We will share the questions later on, but if this problem persists let us know.
00:35:40 Anon. Autograd: Ok thank you, I will.
00:36:25 Anon. Algorithm: no inflection point
00:39:45 Anon. Weight: 0
00:39:47 Anon. Markov Chain: 0
00:40:41 Anon. Layer: constant value
00:40:42 Anon. Giant Squid Neuron: 0
00:44:14 Anon. Indifferentiable: tangent hyperplane to the function?
00:44:40 Anon. Giant Squid Neuron: Tangent vector
00:44:45 Anon. Autograd: The direction of fastest increase
00:44:47 Anon. Max Pool: direction increase faster
00:44:48 Anon. Markov Chain: the direction to which you should travel along that can increase the value of y most quickly
00:47:15 Anon. Layer: 0
00:47:15 Anon. Supervised: 0
00:47:15 Anon. Unsupervised: 0
00:47:16 Anon. Algorithm: 0
00:47:17 Anon. Voltage-gate: 0
00:47:17 Anon. Notagrad: 0
00:47:30 Anon. Residual: 90
00:47:30 Anon. Algorithm: 90
00:47:31 Anon. Loss Function: 90
00:47:32 Anon. Supervised: 90
00:47:33 Anon. Weight: 180
00:47:34 Anon. Unsupervised: 180
00:47:38 Anon. Giant Squid Neuron: 180
00:47:39 Anon. Loss Function: 180
00:47:41 Anon. Residual: 180
00:49:02 Anon. Giant Squid Neuron: Same direction
00:49:09 Anon. Weight: Same as the delta vector
00:53:56 Reshmi Ghosh (TA): Poll folks
00:54:11 Anon. Autograd: I can't see it please
00:54:22 Reshmi Ghosh (TA): Damn
00:54:27 Anxiang Zhang (TA): 5 seconds
00:54:35 Reshmi Ghosh (TA): I will get back to you after the lecture @Dereje
00:54:49 Anon. Sample: Try updating your zoom?
00:54:51 Anon. Transformer: since we’re waiting, I think the slides posted online are not the same as what are being shown
00:55:03 Reshmi Ghosh (TA): Yes they do get changed
00:55:26 Reshmi Ghosh (TA): We modify things until the last minute:) because who doesn’t?
00:55:35 Anon. Dropout: This is Last part of slides for Lec 3
00:56:08 Reshmi Ghosh (TA): BY THE WAY, When Bhiksha says I won’t explain it but will use it ~ potential quiz question. You should refer these slides back. Although no guarantee that it will appear in Quiz 3 (as I haven’t seen the questions myself ):P
00:57:57 Anon. Voltage-gate: -
00:57:58 Anon. Spiking NN: negative
00:57:58 Anon. Markov Chain: neg
00:57:58 Anon. Unsupervised: -ve
00:58:00 Anon. Weight: negative
00:58:01 Anon. Supervised: neg
00:58:10 Anon. Spiking NN: right
00:58:43 Anon. Weight: +
00:58:45 Anon. Supervised: +
00:58:55 Anon. Giant Squid Neuron: -<
00:58:58 Anon. Weight: left
00:58:59 Anon. Supervised: left
00:59:02 Anon. LTI: left
00:59:04 Anon. Leakage: left
01:00:29 Anon. Voltage-gate: xk - step*deltax
01:03:14 Anon. Dropout: large
01:03:23 Anon. Giant Squid Neuron: big
01:03:25 Anon. Markov Chain: big
01:03:41 Anon. LR: small
01:03:43 Anon. Leakage: mall
01:03:43 Anon. Voltage-gate: small
01:03:53 Anon. LR: overshhot
01:11:14 Anon. YOLOv2: step
01:38:21 Anxiang Zhang (TA): 5 seconds