文件名称:简介到深度学习
文件大小:13.63MB
文件格式:ZIP
更新时间:2024-03-18 00:18:12
JupyterNotebook
HSE深度学习简介 此仓库目前包含编程任务!!! 课程于八月更新。 此仓库也包含新编程任务的解决方案!!! 第一周 :-线性模型与优化 第二周 :-带有TF的MNIST数字分类 _2_2:-Numpy NN(荣誉) 第三周 :-您在CIFAR-10上的第一个CNN :-对花的分类微调Inception V3 第四周 :-简单的自动编码器 ; -生成对抗网络(Hohttps://github.com/AKASH2907/Introduction_to_Deep_Learning_Coursera/blob/master/Week_6_Final_PA/my_week6_final_project_image_captioning_clean.ipynbnors) 第五周 :-使用RNN生成名称 第六周 :-关于图像字幕的最终项目
【文件预览】:
introdction-to-deep-learning-master
----Week_6_Final_PA()
--------week6_final_project_image_captioning_clean.ipynb(10.22MB)
--------week6_final_project_image_captioning_clean.py(27KB)
--------my_week6_final_project_image_captioning_clean.ipynb(4.87MB)
----Week_4_PA1()
--------Autoencoders_task.ipynb(689KB)
--------autoencoders_task.py(19KB)
----Week_4_PA2()
--------adversarial_task.py(8KB)
--------Adversarial_task.ipynb(900KB)
----Week_3_PA2()
--------week3_task2_fine_tuning_clean.py(12KB)
--------week3_task2_fine_tuning_clean.ipynb(608KB)
----Week_2_PA_2()
--------NumpyNN+%28honor%29.py(19KB)
--------NumpyNN+%28honor%29.ipynb(83KB)
----Week_5_PA1()
--------RNN_task.ipynb(64KB)
--------rnn_task.py(14KB)
----README.md(2KB)
----Week_2_PA_1()
--------digits_classification.py(9KB)
--------digits_classification.ipynb(78KB)
----Week_1_PA()
--------week01_pa.py(12KB)
--------week01_pa.ipynb(240KB)
----Week_3_ PA1()
--------week3_task1_first_cnn_cifar10_clean.ipynb(665KB)
--------week3_task1_first_cnn_cifar10_clean.py(17KB)