学生对 IBM 提供的 Deep Learning with PyTorch 的评价和反馈

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课程概述

This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using PyTorch. This comprehensive course covers techniques such as Softmax regression, shallow and deep neural networks, and specialized architectures, such as convolutional neural networks. In this course, you will explore Softmax regression and understand its application in multi-class classification problems. You will learn to train a neural network model and explore Overfitting and Underfitting, multi-class neural networks, backpropagation, and vanishing gradient. You will implement Sigmoid, Tanh, and Relu activation functions in Pytorch. In addition, you will explore deep neural networks in Pytorch using nn Module list and convolution neural networks with multiple input and output channels. You will engage in hands-on exercises to understand and implement these advanced techniques effectively. In addition, at the end of the course, you will gain valuable experience in a final project on a convolutional neural network (CNN) using PyTorch. This course is suitable for all aspiring AI engineers who want to gain advanced knowledge on deep learning using PyTorch. It requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices....

26 - Deep Learning with PyTorch 的 27 个评论（共 27 个）

创建者 Jason M

•

Sep 30, 2025

grading on final exam has an example image that contradicts the written instructions I lost a day of my life resubmitting this over and over, with the correct answer getting graded as incorrect. Fix this by changing the image in the last question, to show the lesser accuracy graph , which is written in words as min 85%, but is shown as 89% confusing the graders