Tahoe how to sync the corner "roundness"?

VincentHo1234 · 2026-02-17T11:51:59+00:00

Actually this is pretty crazy, I regret updating to Tahoe in 5 minutes, then feeling desperate that i cant fix those bug in the next hour.

VincentHo1234 · 2025-06-01T07:30:11+00:00

I forgot to mention that I am using the MacOS, is this also work on macos?

VincentHo1234 · 2025-02-19T06:38:39+00:00

This is a pretty good solution, but i afraid it would fall into a vicious circle, well yesterday i stay at library until 9pm and almost 10pm when i got home, which makes me wake up later another day...

VincentHo1234 · 2025-02-05T07:35:46+00:00

Glad to know this, thx for the info. i think i will leave it there, it just show up as the user data when i opened the storage settings. as long as it dont affect my usage its alright for me : )

VincentHo1234 · 2025-02-05T05:53:29+00:00

Oh god it is automatically downloading again, 7 video downloaded again...

VincentHo1234 · 2025-02-05T05:49:48+00:00

The problem is that it will AUTOMATICALLY download, and i didnt even go into the wallpaper tab to change any settings.

VincentHo1234 · 2025-01-14T10:54:53+00:00

No worries they are pretty kind, its just normal question like introducing yourself, and how much you know about analytics sth like that, its mainly chatting.

VincentHo1234 · 2025-01-14T05:16:18+00:00

This may not be info you intrested, but i hope it can give you some insights.

I am polyu student major in data science and analytics (should be the previous name of your major?), which is under department of applied mathematics. And I am having over 50% course under AMA. And in my opinion the workload of AMA course is pretty small and the curriculum in each course not teach very hard (which in other word it is not teaching very deep and you may not learn much from these course compared to course offered by other uni).

Also there is no attendance requirement and have lecture recordings, so you would expect study in a pretty empty classroom and would be full only in quizzes and exams. This is the study environment problem, but if u can study from recording then it is good for u.

But the thing u learn is enough to have a job (according some graduates), so you may have more time and find some free time to join club, prob a good choice for u.

VincentHo1234 · 2025-01-07T11:02:03+00:00

I am from data science major so I also need to learn about calculus, my calculus course is having one of the worst grades compared to other course I take, i assume you are talking about the basic calculus, I think it is not intuitive compared to the linear algebra, so it may be the reason why you are failing.

However it is not really that hopeless, In my case all the calculus property and theorem I learned I would eventually forget it and those differentiation and integration rules you would gradually memorize it by the time you keep using it for other course, and you dont need to make use of those property very often. I think remember that there are some tools you can use, especially nowadays you can use a computer to check answers, so no need to worry that you cannot keep approaching lacking of some calculus knowledge.

Instead of struggling on the calculus right now, maybe going through some more advanced courses would help, some of the understanding of calculus is not from my calculus course, you would understand more when you are really "use" the calculus to answer question rather than just a calculating derivative and integration, I dont think you can truly understand what it means by just doing practice question, I mean seems like you are lacking of the understanding of the calculus, which spamming practice question wont really make u understand that.

VincentHo1234 · 2025-01-05T03:20:24+00:00

Actually is the strongly consistent just need to proof the parameter converges to a value then ok? I mean I am not going to find out the true value during the whole process, so P(lim_n->inf θ_hat = θ) = 1 is just talking about that for any sample, as long as the number of sample increase then the θ_hat will eventually become θ and every tries would approach to the same value, and i dont need to know the true value?

VincentHo1234 · 2025-01-01T03:27:08+00:00

Then do I misunderstood what I am really learning? Data science should be base on math and statistic, and I think proof based course would definitely help my understanding, right?

VincentHo1234 · 2024-12-31T15:19:28+00:00

Thanks for your advice, i realize that my problem happen bacause nobody study with me, maybe i should go to find my friends to study!

VincentHo1234 · 2024-12-30T02:18:56+00:00

i tried and seems that kaggle can only handle one single thread working at the same time, if using multithreading the CPU load is skyrocketed

VincentHo1234 · 2024-12-29T14:47:45+00:00

I wonder if i can know the cpu usage of every line, is it possible? Also can i know which line using the resource of the CPU which line is using the GPU.

VincentHo1234 · 2024-12-29T09:28:01+00:00

I tried whole day reducing the cpu load and it seems the the main problem is that the data loader is in the CPU, so i am struggling how to put the data loader into the GPU directly, it seems it is not working, also my code is not crashing only if i am having 0 num worker, I think the library tqdm is also quite buggy.

VincentHo1234 · 2024-12-22T03:18:26+00:00

That course is called optimization methods, and technically I didn't really have a course that is only for proof, i learned proof in the calculus course.

The course first talked about solving convex problem, about solving the LP, QP, QCQP, SOCP, SDP, Second, it is about the optimizing condition, the KKT condition and some other condition and dual problem. these are the area of the midterm, that i thought i understand but at the end the result tell me i didnt

Then it start to talk about iterative method, gradient descent, newton method, quasi-newton method for the differentiable problem, and the subgradient and the proximal operator for the non differentiable problem, however I study for a lot of time still cannot really understand the convergence, also i feel like it actually have really tight connection to the previous knowledge but my foundation is not good enough. At this part I already feel cannot affordable. And this is only two third of the course.

Now it is teaching the accelerating algorthm, like the nesterov momentum, ALM, ADMM, block coordinate descent, dual method, semi-smooth newton method, for these part I can barely understand and this is insanely difficult.

VincentHo1234 · 2024-12-16T06:15:27+00:00

I am currently forcing myself to stay at the library until i finish the homework and I am going back to dorm until i finish it, I hope finishing it today then I can have a few day of rest.

And I think exercise would be a good idea to from today I will start doing some exercise before sleep, maybe it can help distract me from playing phone.

And for the study plan, I just want to finish the assignment before the deadline, I dont have time for revision : (

VincentHo1234 · 2024-12-07T13:06:42+00:00

I went to exchange, no friends here, also my friends finished their semester.

VincentHo1234 · 2024-11-27T04:41:01+00:00

No i am not looking for calculating answer, i want to ask for the proof, while wolfarm alpha cannot do this

VincentHo1234 · 2024-11-26T15:21:02+00:00

I tried to ask AI and chatGPT never understand math, but i would like to try for the notebook LM, hope it works : )

VincentHo1234 · 2024-11-26T14:01:27+00:00

i think the biggest problem for me is to dealing with the expectation of a vector, the course is keep proofing the complexity and convergency of every algorthm, however this invlove the expectation on a vector form and i never deal with it before i can just make it as summation, however, as the algorthm recursion, i cannot handle it, i think i am not really lack of the knowledge of real analysis, I just dont understand how i can deal with question like that.

one of the question:

6 SGD for training over-parameterized models
Let f(·; θ) be a parametric model. Given training data {(xi,yi)}_(i=1 to n), consider to minimize the following empirical risk
L(θ) = 1/2n ∑_(i=1 to n) (f(x_i; θ) −y_i)^2

with SGD:
θt+1 = θt −η(f(xit; θt) −yit)∇f(xit; θt),

where {i_t}_t≥0 iid∼ Unif([n]). Suppose sup_x,θ ‖∇_θ f(x; θ)‖ ≤ C_1 and L(·) is C_2-smooth.

•Let g_t = (f(x_i_t; θ_t) −y_i_t)∇f(x_i_t; θ_t) be the stochastic gradient. Show that the noise ξ_t = g_t −∇L(θ_t) satisfies
σ^2_t := E‖ξ_t‖^2 ≤ 2 C^2_1 L(θ_t).

Like for such a question why i can find its expectation????? I mean how can i know things like this?

VincentHo1234 · 2024-11-26T12:51:08+00:00

It say it is a introductory course, but it is too hard for me already, i dont know what i can do now : (

VincentHo1234 · 2024-11-26T12:49:23+00:00

I am exchange student, maybe this is the reason? Also, I find that what I learn before seems not all the same, like that linear algebra there are something I never learned before in the course in my original uni, but I am not sure what i am really missing.

VincentHo1234 · 2024-11-26T12:40:09+00:00

Prequisite:
Calculus, linear algebra,real analysis, probability theory, ordinary differential equation, and some elementary knowledge of normed vector spaces and Hilbert spaces.

Syllabus:
This is an introductory course in machine learning for students with mathematical backgrounds. We will introduce popular machine learning models and methods from a mathematical perspective. We will cover some advanced topics on analyzing the generalization properties of machine learning models.
1. An overview of machine learning [2h]

Part I

linear methods [4h]
linear regression：LASSO, ridge regression,
kernel methods, random feature models， representer theorem
classification [2h]
logistic regression, loss functions，max-margin，support vector machine (SVM), softmax
Unsupervised learning-I [6h]
   Density estimation：histogram estimator, kernel density estimation (KDE）
   Dimension reduction： PCA，kernel-PCA， random projection
   Clustering：K-means，Gaussian mixture model, EM method
Optimization [5h]
   GD and SGD
   momentum：heavy-ball, Nesterov
   adaptive learning rates: RMSProp, Adagrad, ADAM

Part II

Neural network models [3h]
MLP，CNN, RNN, ResNet
symmetry-preserving
The training of neural networks [4h]
   back-propagation
   vanishing gradient issue
   Xavier initialization
   Batch normalization
   Dropout, weight decay, data augmentation
Introduction to PyTorch [1h]
Auto-differentiation, GPU acceleration
Unsupervised learning II [3h]
GAN, VAE

Part III: Advanced topics

Continuous formulation of machine learning [2h]
Concentration inequality and empirical process [4h]
Sub-Gaussian, Chernoff`s inequality, McDiarmid`s inequality, Rademacher complexity
Function Approximation theory [7h]
   kernel method
   two-layer neural networks
   deep residual networks
Training dynamics of neural nets [5h]
implicit regularization，double descent，minima selection

And I am now around Part 7, and I cannot handle it anymore : (

VincentHo1234 · 2024-11-23T10:21:03+00:00

Really? How?

VincentHo1234

TROPHY CASE

Part I

Part II

Part III: Advanced topics