Daesoo Lee's Blog

Similar to "requirements.txt", Anaconda provides an exporting option for its environment (i.e., installed libraries) as "environment.yml". You can simply run conda env create -f environment.yml Then, you'd get something like: Reference: https://www.anaconda.com/blog/moving-conda-environments

Input Target Formatting Reference: https://github.com/zhouhaoyi/Informer2020/blob/main/data/data_loader.py (refer to __getitem__(..))

Architecture Explained: https://towardsdatascience.com/unet-line-by-line-explanation-9b191c76baf5 Loss function Explained: https://towardsdatascience.com/u-net-b229b32b4a71

Example: https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall Classification: Precision and Recall | Machine Learning Crash Course | Google Developers Google is committed to advancing racial equity for Black communities. See how. Send feedback Classification: Precision and Recall Estimated Time: 9 minutes Precision Precision attempts to answer the follo..

1. https://youtu.be/j6kuz_NqkG0 2. https://youtu.be/TrdevFK_am4 3. What is the Class Token? One of the interesting things about the Vision Transformer is that the architecture uses Class Tokens. These Class Tokens are randomly initialized tokens that are prepended to the beginning of your input sequence. What is the reason for this Class Token and what does it do? Note that the Class Token is ra..

1. https://youtu.be/z1xs9jdZnuY 2. https://youtu.be/4Bdc55j80l8 About Positional Encoding https://kazemnejad.com/blog/transformer_architecture_positional_encoding/ Transformer Architecture: The Positional Encoding - Amirhossein Kazemnejad's Blog Transformer architecture was introduced as a novel pure attention-only sequence-to-sequence architecture by Vaswani et al. Its ability for parallelizabl..

https://www.youtube.com/watch?v=4vGiHC35j9s&ab_channel=NandodeFreitas

In the variational auto encoder (VAE) (my blog posting link), a probability of observing $z$ given $x$ can be presented as follows using Bayes' theorm: $$ p(z | x) = \frac{ p(x|z) p(z) }{ p(x) } $$ where $p(z|x)$ is a posterior. However, this posterior is difficult to compute due to its marginal likelihood $p(x)$. $p(x)$ can be expanded as below using the law of total probability: $$ p(x) = \int..