
學員將學會如何開始學習 AI
ML與DL
學習DL需要什麼條件
如何使用Pytorch-線上版
如何安裝Pytorch到本地電腦上
Windows安裝補充
JupyterLab的優點及使用
什麼是Tensor它有什麼用處
隨機Tensor
Zeros_like & Arange
數據類型- Float浮點數類型
Int & Bool & Complex數據類型
小練習1
View & Reshape & Flatten的區別
運算符號
mul&mm&matmul
矩陣相乘約束
Broadcasting廣播
Min()&max()&mean()&sum()
Argmin&Argmax&where
Stack&vstack&hstack
Squeeze&unsqueeze
Permute
Tensor索引
Numpy & Tensor轉換
隨機數種子
Class類
Class初始化&繼承&多態
為什麼需要CUDA
CPU To GPU
對比CPU與GPU的運行速度
什麼是Perception感知器
用Python製作感知器
Error Function誤差函數
用Python製作損失函數
Gradient Descent剃度下降
大家好,我是Ken Cen,鑑於上一部機械學習課程大受歡迎,我們再推出Machine Learning的第二部分內容。在這一部中,我們將講述Pytorch如何實現深度學習的各種演算法,以及如何具體實踐於Python當中。
演算法作為學習Machine Learning的重點和難點,一直以來,讓很多學生望而卻步。我們的Machine Learning課程正正為此而生。相對於輕輕帶過,我們課程會將重點放在演算法的理解上,為大家打開ML學習的大門。期待在課程中,與大家一起從零到專業學習人工智能!
Hello everyone, I'm Ken Cen. Following the great success of our previous machine learning course, we are launching the second part of our Machine Learning content. In this course, we will be covering various deep learning algorithms and how to implement them in Python.
Algorithms are a key and difficult aspect of learning Machine Learning, which has made many students shy away from it. Our Machine Learning course was created specifically for this reason. Instead of brushing over the topic, our course will focus on understanding the algorithms and open the door to learning ML for everyone. We look forward to learning artificial intelligence together with you from beginner to professional in this course!