在凌晨三点时刻, 终于将这份关于B站学习的避坑指南整理完成了。真心不期望后续之人再耗费大量时间逐一去尝试错误, 于海量教程之中不清楚该如何挑选, 又或者由于基础欠佳而看得糊里糊涂。B站之上有着数量众多的教程, 究竟哪一个才契合你呢? 实际上每一个都存有着自身的优点, 精准地寻找到自身的定位才是最为关键的。好多人一开始就去啃那个播放量处于最高态势的小土堆推出的《深度学习快速入门教程》。这门课程讲授的状况着实称得上不错, 然而呢, 它愈发像一本专注于讲述操作行径的“说明书”, 从施行数据加载流程一直到达成结果可视化这般的整个阶段, 其间所涉及的操作方面的详细具体情形可谓全部展现出来了。要是没有基础以及深度学习理论作为支撑, 极易看得糊糊模模, 似懂非懂。毕竟每个操作都和神经网络的流程相对应, 不懂理论压根看不懂操作。对于纯身为毫无基础且时间紧迫的新人来讲, 备受赞誉的刘二大人那里的《深度学习实践》或许更能契合需求。这门课程的关键要点在于其讲解的逻辑脉络, 它会将相关原理以及代码实操紧密地融合编织于一处。先扔出一个特定问题来引出知识点, 使你清楚这东西有着怎样的用途, 接着通过代码去呈现原理, 学习时反馈的那种感觉极为强烈, 算得上几乎不存在什么门槛就能够看得懂这种展现形式。要是你有着充裕的时间, 或者你研究的范畴是图像影像类的数据, 那么炮哥所著的《框架与经典卷积神经网络与实战》是十分值得去看一看的。这是B站同类型课程里, 理论讲解数量最多且最为系统的一门课程。它会引领你先是从最简单的全连接神经网络原理着手在弄明白根基之后, 接着借助卷积神经网络的一个个原理, 所引出的代码操作, 最终利用两个完整的小项目, 带你贯通实战的全部流程, 同样对毫无基础的人极为友好。入门通过关卡之后, 要是想让操作更加精湛、进阶, 能够瞅瞅一位称作deep的UP主所制作的《有关源码教程以及前沿人工智能算法复现的讲解内容合集》。只需要挑选跟自身研究方向相契合的模型去进行复现查看便可由于他同样是将模型原理与代码操作结合起来讲解的, 理论以及实操都能够学到手。无数新手为之抓狂的第一道坎是环境配置。解决办法实际上就只有两个: 其一, 直接前往小土堆课程的评论区里面去搜, 或者到弹幕里去搜, 这门课程的播放量是最高的, 几乎你所能遇到的坑, 都有人踩越过并且留下了相应的解决方案。一, 是去看一个名为“我不是电子厂”的UP主所发布的环境配置视频, 二, 这位UP主会将大家在环境配置过程中遇到的常见问题逐个进行演示, 三, 并且把这些问题一一解决掉。那就来说说那个刚开始入门所需要具备的前期基础条件, 所需的仅仅是要有那种最为基础的数学方面的知识底子, 还有编程领域的基础, 另外还得知晓一些NumPy, 以及需要明白这两个库的基本操作方式, 并且也要清楚全连接神经网络的大概原理情况, 像这样就完全足够能够看懂大多部分的教程了。花上一两周的时间, 便能够掌握。有一些同学, 觉得小土堆的课看的时候很吃力, 原因在于他们的和深度学习理论是“散装”的, 在这个时候, 可以先去看看口碑特别好的“鱼书”, 其中对于基础和深度学习理论的铺垫正好是够用的, 学完之后再去看小土堆, 一下子就明白通透了。要不跟着那个爆肝杰哥所制作的深度学习合集走上一回, 该合集中, NumPy以及基础理论全部涵盖在内。于搭建神经网络之际, 激活函数之选择以及理解, 是极其关键重要的。比如说, 当下最为受人欢迎喜爱的ReLU函数, 它是暗藏着一个所谓“神经元坏死”的问题呢: 要是输入全部俱为负数, 那么它的输出将会始终永远是0, 其梯度同样也是0, 如此一来这个神经元就“死”掉了, 往后再也没有办法进行更新。当应对网络过拟合进而需去实施解决举措的时候, 层于训练这个阶段会类比抽奖的情况, 以随机的方式“关闭”若干神经元, 而如此做的效用乃是强制性使得网络就更具鲁棒性的特征展开学习, 不过要予以留意的是, 在最终预测之际要记得切换至专门用于评估的模式下进行, 不然的话, 所产生的结果将会呈现随机变化的状况。当下, 当你历经辛苦总算成功且稳步入之门径后, 在后阶段的学习规划便能够顺着一条十分明确的路线图形进, 最终, 那些历经多年积累所形成的合计为一百四十二份的优质相关资源库, 也已完成分类规整, 对于入门之后的进一步提升起着莫大的协助作用, 期望此份指南, 可以令你于学习之径上, 前行得更加平稳一点。于你所看过的这般B站教程里, 以及自身寻觅摸索的进程当中, 你又可曾碰到过哪些能使人抓狂不已的“怪异”坑? 快来评论区直抒己见, 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