唐宇迪-零基础入门实战深度学习Pytorch
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 唐宇迪-零基础入门实战深度学习Pytorchhttps://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 058-2-文本数据处理基本流程分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 009-8-神经元个数的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 008-7-神经网络效果可视化分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 035-7-参数对结果的影响.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 050-8-模型训练方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 056-3-Dataloader中需要实现的方法分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 016-6-池化层的作用与效果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 042-3-卷积网络模型训练.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 011-1-卷积神经网络概述分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 024-6-位置编码与解码器.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 020-2-注意力结构历史故事介绍.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 034-6-训练一个基本的分类模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 065-9-模型训练任务与总结.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 022-4-QKV的来源与作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 037-2-参数初始化操作解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 003-2-模型更新方法解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 040-1-输入特征通道分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 054-1-Dataloader要完成的任务分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 052-10-测试结果演示分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 007-6-神经网络整体架构详细拆解.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 043-1-任务分析与图像数据基本处理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 023-5-多头注意力机制的效果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 027-1-PyTorch框架与其他框架区别分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 033-5-损失与训练模块分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 012-2-卷积要完成的任务解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 036-1-任务与数据集解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 046-4-迁移学习方法解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 077-8-损失计算与训练.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 025-7-整体架构总结.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 048-6-输出类别个数修改.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 067-2-服务端处理与预测函数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 057-1-数据集与任务目标分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 014-4-层次结构的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 002-1-神经网络要完成的任务分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 068-3-基于Flask测试模型预测结果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 039-4-模型学习与预测.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 053-4-实用Dataloader加载数据并训练模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 061-5-预料表与字符切分.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 071-2-源码DEBUG演示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 006-5-反向传播演示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 066-1-基本结构与训练好的模型加载.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 059-3-命令行参数与DEBUG.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 044-2-数据增强模块.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 038-3-训练流程实例.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 021-3-self-attention要解决的问题.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 018-8-经典网络架构概述.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 010-9-预处理与dropout的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 013-3-卷积计算详细流程演示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 045-3-数据集与模型选择.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 074-5-QKV计算方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 015-5-参数共享的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 055-2-图像数据与标签路径处理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 063-7-LSTM网络结构基本定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 041-2-卷积网络参数解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 073-4-分块要完成的任务.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 005-4-前向传播流程解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 001-课程介绍.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 062-6-字符预处理转换ID.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 032-4-数据源定义简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 060-4-训练模型所需基本配置参数分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 051-9-重新训练全部模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 049-7-优化器与学习率衰减.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 017-7-整体网络结构架构分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 047-5-输出层与梯度设置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 019-1-RNN网络结构原理与问题.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 031-3-网络结构定义方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 076-7-完成前向传播.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 028-2-CPU与GPU版本安装方法解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 075-6-特征加权分配.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 030-2-基本模块应用测试.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 070-1-项目源码准备.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 069-1-视觉transformer要完成的任务解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 026-8-BERT训练方式分析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 064-8-网络模型预测结果输出.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 004-3-损失函数计算方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 029-1-数据集与任务概述.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 072-3-Embedding模块实现方法.mp4
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