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斯坦福大学机器学习课程原始讲义
- 新手入门机器学习，深度学习，AI，大数据，首选吴恩达教授的入门讲义(Beginners, machine learning, depth learning, AI, big data, the first selection of Professor Wu Enda Lecture Notes)
assignment1
- 吴恩达深度学习 第二课第一周python 作业 附答案(sdfsdfdscsdvdsvdscdcs)
第二周
- 吴恩达 deep Learning 第一颗第二周题目 附答案(dsgsgfdfddfdfrthre deeplearning)
01-第一课 神经网络和深度学习
- 吴恩达人工智能课程，第一课神经网络和深度学习的课后练习题以及答案(The Andrew Ng artificial intelligence course, the first lesson of the neural network and the intensive study exercises and the answers)
斯坦福大学-深度学习基础教程
- 斯坦福大学的深度学习，基础教程。ps:要求先学完吴恩达的机器学习基础再来看，不然看不懂(Depth study of Stanford University, basic course. Ps: needs to learn the basis of Wu Enda's machine learning first, or you can't understand it.)
Prog Asgn of DL Specialization courses
- 吴恩达深度学习笔记，这个不错，值得看看，会有帮助的(Note book of deep learning, it is good enough. Yes.)
吴恩达深度学习基础教程
- 吴恩达教授有关深度学习浅显易懂的算法介绍，包括基础算法和示例。(Deep learning algorithms introduction by Andrew Ng.)
序列模型
- 吴恩达，DeepLearning.ai，第四课序列模型相关papers收集整合(Andrew NG, DeepLearning.ai, fourth lesson sequence model related papers collection and integration)
吴恩达深度学习基础教程
- 吴恩达博士是Google Brain项目的发起人和领导者，斯坦福大学的计算机科学教授，Coursera的联合创始人和联合主席。他还曾任百度的副总裁和首席科学家，(Dr. Wu Enda is the founder and leader of the Google Brain project, a professor of computer science at the Stanford University, co - founder and co - chair of the Courser
Coursera深度学习笔记v4
- 人工智能大师吴恩达的深度学习课程，整理资料。很好地材料。(Artificial intelligence master Wu Enda's deep learning course, collate information. Good material.)
吴恩达深度学习py文件+笔记
- 自己做的吴恩达的深度学习课的笔记，py文件实现等，笔记是在边编程边写出来的，跳过了很多坑，所有写出来的py文件都能直接用，直接执行
1_Neural Network & DeepLearning
- 吴恩达深度学习微专业课程一配套作业，jupyter notebook格式的(Wu enda deep learning micro professional course a matching homework)
神经网络基础
- 神经网络基础ppt,课件来源于吴恩达老师深度学习课程课件(Ppt of neural network foundation, the courseware comes from the courseware of in-depth learning of teacher Wu enda)
吴恩达深度学习基础教程
- 本教程将阐述无监督特征学习和深入学习的主要观点。通过学习，你也将实现多个功能学习/深度学习算法，能看到它们为你工作，并学习如何应用/适应这些想法到新问题上。(This tutorial will explain the main points of unsupervised feature learning and in-depth learning. Through learning, you will also implement multiple functional learning /