深度学习与PyTorch入门实战教程（价值399元）

1.深度学习框架介绍 [48.66M]

1.lesson1-pytorch介绍.mp4 [48.66M]

10.卷积神经网络cnn [678.51M]

50.lesson37-什么是卷积-1.mp4 [62.76M]

51.lesson37-什么是卷积-2.mp4 [39.60M]

52.lesson38-卷积神经网络-1.mp4 [41.36M]

53.lesson38-卷积神经网络-2.mp4 [62.89M]

54.lesson38-卷积神经网络-3.mp4 [35.46M]

55.lesson39-pooling&upsample.mp4 [34.05M]

56.lesson40-batchnorm-1.mp4 [41.45M]

57.lesson40-batchnorm-2.mp4 [51.27M]

58.lesson41-lenet5,alexnet, vgg, googlen.mp4 [49.28M]

59.lesson41-lenet5,alexnet, vgg, googlen.mp4 [40.38M]

60.lesson42-resnet，densenet-1.mp4 [53.18M]

61.lesson42-resnet, densenet-2.mp4 [43.56M]

62.lesson43-nn.module-1.mp4 [44.98M]

63.lesson43-nn.module-2.mp4 [31.43M]

64.lesson44-数据增强data argumentation.mp4 [46.85M]

11.cifar10与resnet实战

12.循环神经网络rnn&lstm [465.04M]

65.lesson46-时间序列表示.mp4 [53.55M]

66.lesson47-rnn原理-1.mp4 [28.41M]

67.lesson47-rnn原理-2.mp4 [34.94M]

68.lesson48-rnn layer使用-1.mp4 [34.23M]

69.lesson48-rnn layer使用-2.mp4 [29.91M]

70.lesson49-时间序列预测.mp4 [53.30M]

71.lesson50-rnn训练难题.mp4 [55.00M]

72.lesson51-lstm原理-1.mp4 [32.97M]

73.lesson51-lstm原理-2.mp4 [45.70M]

74.lesson52-lstm layer使用.mp4 [28.45M]

75.lesson53-情感分类实战.mp4 [68.58M]

13.对抗生成网络gan [316.24M]

76.lesson54-数据分布.mp4 [17.44M]

77.lesson55-画家的成长历程.mp4 [28.85M]

78.lesson56-gan发展.mp4 [23.03M]

79.lesson57-纳什均衡-d.mp4 [20.42M]

80.lesson58-纳什均衡-g.mp4 [36.65M]

81.lesson59-js散度的弊端.mp4 [36.81M]

82.lesson60-em距离.mp4 [17.16M]

83.lesson61-wgan与wgan-gp.mp4 [28.84M]

84.lesson62-g和d实现.mp4 [17.28M]

85.lesson63-gan实战.mp4 [33.30M]

86.lesson64-gan训练不稳定.mp4 [20.20M]

87.lesson65-wgan-gp实战.mp4 [36.27M]

2.开发环境准备 [54.47M]

2.lesson2-开发环境准备.mp4 [54.47M]

3.初见深度学习 [208.57M]

3.lesson3-初探linear regression案例-1.mp4 [71.93M]

4.lesson3-初探linear regression案例-2.mp4 [43.15M]

5.lesson4-pytorch求解linear regression案例.mp4 [35.72M]

6.lesson5 -手写数字问题引入1.mp4 [36.74M]

7.lesson5 -手写数字问题引入2.mp4 [21.03M]

4.pytorch张量操作 [426.38M]

10.lesson7 创建tensor 1.mp4 [51.58M]

11.lesson7 创建tensor 2.mp4 [44.27M]

12.lesson8 索引与切片1.mp4 [47.24M]

13.lesson8 索引与切片2.mp4 [45.41M]

14.lesson9 维度变换1.mp4 [33.07M]

15.lesson9 维度变换2.mp4 [40.69M]

16.lesson9 维度变换3.mp4 [40.76M]

17.lesson9 维度变换4.mp4 [40.80M]

8.lesson6 基本数据类型1.mp4 [54.35M]

9.lesson6 基本数据类型2.mp4 [28.20M]

5.张量高阶操作 [405.26M]

18.lesson10 broatcasting 1.mp4 [57.86M]

19.lesson10 broatcasting 2.mp4 [46.18M]

20.lesson11 合并与切割1.mp4 [46.78M]

21.lesson11 合并与切割2.mp4 [30.81M]

22.lesson12 基本运算.mp4 [67.11M]

23.lesson13 数据统计1.mp4 [39.94M]

24.lesson13 数据统计2.mp4 [54.70M]

25.lesson14 高阶op.mp4 [61.86M]

6.随机梯度下降 [286.11M]

26.lesson16 什么是梯度1.mp4 [69.17M]

27.lesson16 什么是梯度2.mp4 [43.31M]

28.lesson17 常见梯度.mp4 [18.38M]

29.lesson18 激活函数及其梯度1.mp4 [45.53M]

30.lesson18 激活函数及其梯度2.mp4 [44.38M]

31.lesson18 激活函数及其梯度3.mp4 [65.34M]

7.感知机梯度传播推导 [258.27M]

32.lesson19 单一输出感知机1.mp4 [47.44M]

33.lesson19 多输出loss层2.mp4 [49.69M]

34.lesson20 链式法则.mp4 [39.94M]

35.lesson21 反向传播.mp4 [82.01M]

36.lesson22 优化小实例.mp4 [39.18M]

8.多层感知机与分类器 [353.89M]

37.lesson24 logistic regression.mp4 [47.84M]

38.lesson25 交叉熵.mp4 [72.77M]

39.lesson26 多分类实战.mp4 [35.00M]

40.lesson27 全连接层.mp4 [52.13M]

41.lesson28 激活函数与gpu加速.mp4 [39.59M]

42.lesson29 测试.mp4 [53.80M]

43.lesson30-visdom可视化.mp4 [52.77M]

9.过拟合 [262.47M]

44.lesson31-过拟合与欠拟合.mp4 [42.48M]

45.lesson32-train-val-test-交叉验证-1.mp4 [45.95M]

46.lesson32-train-val-test-交叉验证-2.mp4 [32.32M]

47.lesson33-regularization.mp4 [39.04M]

48.lesson34-动量与lr衰减.mp4 [51.46M]

49.lesson35-early stopping, dropout, sgd.mp4 [51.23M]