"Work like Hell. Share all you know. Abide by your handshake. Have fun!" - Dan Geer
I saw this on my mentor’s internal profile page some 20 years back, I shamelessly stole it and made it mine .... years later I discovered Dan Geer, the author of this quote ...but it does not matter who said it, rather the key is to assimilate these adages, these words of wisdom in your very being and is equally important to keep them in your sight as the gentle reminder of what is important!
Amongst the many obligations and responsibilities that we all have, the one that remains most dear to me is to keep learning and then sharing what I learned. I have done this for as long as I can remember; very early on in my life, I had accidentally discovered that you learn more when you share what you know. This is one aspect of my life that has been very consistent & the one I cherish the most.
This youtube channel is my new medium of sharing what "I think I know"!
Kapil Sachdeva ji . Thanks very much for clearing my doubt over Bayesian equation inherently using marginal distribution. You really are a great teacher 🎉❤
How can we just sample from the target distribution in order to calculate the acceptance ratio? Doesn't that defeat the purpose of the algorithm: Why wouldn't I just take samples from the target directly? Or is the problem that we are not able to draw independently from the target?
I have a question if it is possible for the sum of probabilities for future state to be greater than 1 as in the case of s3 at 14:04 in video...? It seems it should sum to 1 always.
Can you please guide me whether weight vector is column vector or row vector. It is creating confusion in multiplication. Thanks in advance for the great series.
Have been using Chris Bishop's new DL book and he reuses the same figure from PRML. Thanks for your video, the general equations are crystal clear now! ❤
Still not very clear to me why that hasting generalization is required? It would have been better if you had explicitly pointed out the problem statement with metropolis that requires generalization solution.
What is t1,t2..tn.. are these different instance of target variable? If so why each has different distribution. Are you assuming these categorical target variable and each has it own distribution? This part is confusing me in all the videos.
How is this different from U-net? I think they're pretty similar if you think that in the U-net you're going down in the encoder, up in the decoder and sideways with the skip connections. It's like an upside-down U-net
Thank a lot, this was an amazing explanation! just a question. Why do we need anchor boxes to be a pre-set value when we can just use the original bbox values? Like we use the center of the original bbox and its W and H values. I dont understand this
Thank you for posting this great video! At 3:53, why did you use a "squared" Euclidean distance, instead of an Euclidean distance? I wonder if you use an Euclidean distance, the properties of the "squared" Euclidean distance are the same?
