ComputerScience (329) 썸네일형 리스트형 PL2. Syntax 1. Terms Syntax : form, structure, grammar of expression(statement) Semantic : meaning of the expression(statement) sentence : string of characters language : set of sentences lexeme : lowest level syntatic unit (ex. int, age, =) token : category of lexeme (e.g. identifier, type) Recognizer : parser, parse sentence into a tree, check input wheter it belongs to the language Generator : generate I.. PL1. Programming Languages? 1. What is a programming language? telling computer what to do? Programing Language also can be a mean to communicate an algorithm or to describe a process 프로그래밍 언어는 단순히 컴퓨터에게 할 일을 지시하기 위한 도구가 아니라 어떤 작업의 순서, 과정을 설명하는 의사소통 수단으로의 역할을 한다. 2. How can machine understand a programming language? CPU only understand assembly language consist of 0 and 1. Compiler which is also a program translate a progr.. Installing PyTorch on Apple M1 chip with GPU Acceleration https://towardsdatascience.com/installing-pytorch-on-apple-m1-chip-with-gpu-acceleration-3351dc44d67c Installing PyTorch on Apple M1 chip with GPU Acceleration It finally arrived! towardsdatascience.com Deep Learning - 3.5 Weight Decay Weight decay (commonly called L2 regularization), might be the most widely-used technique for regularizing parametric machine learning models. how should the model trade off the standard loss for this new additive penalty? In practice, we characterize this tradeoff via the regularization constant λ, a non- negative hyperparameter that we fit using validation data given the penalty term alone, ou.. tmux 사용법 https://hbase.tistory.com/200#:~:text=tmux%20%EC%82%AC%EC%9A%A9%EB%B2%95%20%2D%20%EB%8B%A8%EC%B6%95%ED%82%A4%20%EA%B4%80%EB%A0%A8%20%EB%AA%85%EB%A0%B9,-tmux%EC%97%90%20%EB%BF%8C%EB%A0%A4%EC%A7%84&text=copy%20%EB%AA%A8%EB%93%9C%EB%A1%9C%20%EC%A7%84%EC%9E%85%ED%95%98%EB%A9%B4,%EC%9D%B4%EC%A0%84%20%EB%AA%85%EB%A0%B9%EC%96%B4%EA%B0%80%20%EC%84%A0%ED%83%9D%EB%90%9C%EB%8B%A4. [Linux] tmux 설치와 사용법 및 예제.. Deep Learning - 3.4 Model Selection, Underfitting, Overfitting How to discover patterns that generalize is the fundamental problem of machine learning. Our predictions will only be useful if our model has truly discovered a general pattern. When working with finite samples, we run the risk that we might discover apparent associations that turn out not to hold up when we collect more data. The phenomenon of fitting our training data more closely than we fit .. local에서 ssh tunneling으로 원격 서버의 jupyter notebook 접속하기 https://www.digitalocean.com/community/tutorials/how-to-set-up-jupyter-notebook-with-python-3-on-ubuntu-18-04 How To Set Up Jupyter Notebook with Python 3 on Ubuntu 18.04 | DigitalOcean www.digitalocean.com 1. 원격 서버에 jupyter notebook설치 2. local 컴퓨터 terminal에서 원격 서버로 ssh 접속 (이때 터널링 포트 지정) ssh -L 8888:localhost:8888 your_server_username@your_server_ip 3. 원격 서버 에서 jupyter notebook 4. local 컴퓨터 web .. Deep Learning - 3.2~3.3 Implementation of Multilayer Perceptrons This time we'll make deeper neural network which classify MNIST fashion dataset. For the same classification problem, the implementation of an MLP is the same as that of softmax regression except for additional hidden layers with activation functions. 1. Model Typically, we choose layer widths in powers of 2, which tend to be computationally efficient because of how memory is allocated and addre.. 이전 1 2 3 4 5 6 ··· 42 다음
