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微专业 深度学习工程师 吴恩达给你的人工智能第一课
〖课程目录〗:
网易云课堂 微专业 深度学习工程师 吴恩达给你的人工智能第一课 [3.8G]
┣━━01.神经网络和深度学习 [902.9M]
┃ ┣━━1.第一周 深度学习概论 [65.1M]
┃ ┃ ┣━━【1.2课件】What is a neural network-Lecture Notes.pdf [409.5K]
┃ ┃ ┣━━【1.2课件】What is a neural network.pptx [262.3K]
┃ ┃ ┣━━【1.3课件】Supervised Learning with Neural Networks-Lecture Notes.pdf [442.2K]
┃ ┃ ┣━━【1.3课件】Supervised Learning with Neural Networks.pptx [6.5M]
┃ ┃ ┣━━【1.4课件】Why is Deep Learning taking off-Lecture Notes.pdf [424.5K]
┃ ┃ ┣━━【1.4课件】Why is Deep Learning taking off.pptx [237.8K]
┃ ┃ ┣━━【1.5课件】About this Course.pptx [91.4K]
┃ ┃ ┣━━1.1 欢迎.mkv [10M]
┃ ┃ ┣━━1.2 什么是神经网络？.mkv [8.4M]
┃ ┃ ┣━━1.3 用神经网络进行监督学习.mkv [12.5M]
┃ ┃ ┣━━1.4 为什么深度学习会兴起？.mkv [18.2M]
┃ ┃ ┣━━1.5 关于这门课.mkv [4.6M]
┃ ┃ ┣━━1.6 课程资源.mkv [2.4M]
┃ ┃ ┗━━第一周quiz小测验.pdf [599.3K]
┃ ┣━━2.第二周 神经网络基础 [237.7M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【2.11课件】_vectorization_C1W2L07.pptx [373.6K]
┃ ┃ ┣━━【2.13课件】_vectorizing-logistic-regression_C1W2L08.pptx [520.3K]
┃ ┃ ┣━━【2.15课件】_broadcasting-in-python_C1W2L09.pptx [330.7K]
┃ ┃ ┣━━【2.16课件】_a-note-on-python-numpy-vectors_C1W2L09_2.05.53_PM.pptx [93.3K]
┃ ┃ ┣━━【2.1课件】_binary-classification_Binary_Classification.pdf [649.6K]
┃ ┃ ┣━━【2.1课件】_binary-classification_C1W2L01.pptx [4.5M]
┃ ┃ ┣━━【2.1课件】_binary-classification_untitled-2.pdf [255.7K]
┃ ┃ ┣━━【2.2课件】_logistic-regression_C1W2L02.pptx [356.8K]
┃ ┃ ┣━━【2.2课件】_logistic-regression_Logistic_Regression.pdf [522.2K]
┃ ┃ ┣━━【2.3课件】_logistic-regression-cost-function_Logistic_Regression_Cost_Function.pdf [472.4K]
┃ ┃ ┣━━【2.4课件】_gradient-descent_C1W2L03.pptx [892.3K]
┃ ┃ ┣━━【2.5课件】_derivatives_C1W2L04.pptx [375.8K]
┃ ┃ ┣━━【2.7课件】computation-graph_C1W2L05.pptx [369.7K]
┃ ┃ ┣━━【2.9课件】_logistic-regression-gradient-descent_C1W2L06.pptx [438.8K]
┃ ┃ ┣━━2.1 二分分类.mkv [14.9M]
┃ ┃ ┣━━2.10 m 个样本的梯度下降.mkv [11.8M]
┃ ┃ ┣━━2.11 向量化.mkv [12.2M]
┃ ┃ ┣━━2.12 向量化的更多例子.mkv [10.1M]
┃ ┃ ┣━━2.13 向量化 logistic 回归.mkv [11.1M]
┃ ┃ ┣━━2.14 向量化 logistic 回归的梯度输出.mkv [15.1M]
┃ ┃ ┣━━2.15 Python 中的广播.mkv [15.7M]
┃ ┃ ┣━━2.16 关于 python _ numpy 向量的说明.mkv [12.1M]
┃ ┃ ┣━━2.17 Jupyter _ ipython 笔记本的快速指南.mkv [9.2M]
┃ ┃ ┣━━2.18 （选修）logistic 损失函数的解释.mkv [10.2M]
┃ ┃ ┣━━2.2 logistic 回归.mkv [8.2M]
┃ ┃ ┣━━2.3 logistic 回归损失函数.mkv [12.8M]
┃ ┃ ┣━━2.4 梯度下降法.mkv [16.6M]
┃ ┃ ┣━━2.5 导数.mkv [13.1M]
┃ ┃ ┣━━2.6 更多导数的例子.mkv [16.7M]
┃ ┃ ┣━━2.7 计算图.mkv [5.5M]
┃ ┃ ┣━━2.8 计算图的导数计算.mkv [21.4M]
┃ ┃ ┣━━2.9 logistic 回归中的梯度下降法.mkv [10.9M]
┃ ┃ ┗━━第二周quiz小测验.pdf [108K]
┃ ┣━━3.第三周 浅层神经网络 [165.1M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【3.10课件】_backpropagation-intuition-optional_C1W3L08.pptx [501.5K]
┃ ┃ ┣━━【3.11课件】_random-initialization_C1W3L09.pptx [228.7K]
┃ ┃ ┣━━【3.1课件】_neural-networks-overview_C1W3L01.pptx [167.8K]
┃ ┃ ┣━━【3.2课件】_neural-network-representation_C1W3L02.pptx [569.2K]
┃ ┃ ┣━━【3.4课件】_vectorizing-across-multiple-examples_C1W3L03.pptx [544.7K]
┃ ┃ ┣━━【3.6课件】_activation-functions_C1W3L04.pptx [330.2K]
┃ ┃ ┣━━【3.7课件】_why-do-you-need-non-linear-activation-functions_C1W3L05.pptx [213.5K]
┃ ┃ ┣━━【3.8课件】_derivatives-of-activation-functions_C1W3L06.pptx [370.1K]
┃ ┃ ┣━━【3.9课件】_gradient-descent-for-neural-networks_C1W3L07.pptx [304.8K]
┃ ┃ ┣━━3.1 神经网络概览.mkv [7M]
┃ ┃ ┣━━3.10 （选修）直观理解反向传播.flv [39M]
┃ ┃ ┣━━3.10 （选修）直观理解反向传播.srt [16.7K]
┃ ┃ ┣━━3.11 随机初始化.mkv [11.6M]
┃ ┃ ┣━━3.2 神经网络表示.mkv [8M]
┃ ┃ ┣━━3.3 计算神经网络的输出.mkv [15.9M]
┃ ┃ ┣━━3.4 多个例子中的向量化.mkv [13.5M]
┃ ┃ ┣━━3.5 向量化实现的解释.mkv [11.6M]
┃ ┃ ┣━━3.6 激活函数.mkv [19.4M]
┃ ┃ ┣━━3.7 为什么需要非线性激活函数？.mkv [9M]
┃ ┃ ┣━━3.8 激活函数的导数.mkv [11M]
┃ ┃ ┣━━3.9 神经网络的梯度下降法.mkv [15.6M]
┃ ┃ ┗━━第三周quiz小测验.pdf [102.1K]
┃ ┣━━4.第四周 深层神经网络 [110.6M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【4.3课件】_getting-your-matrix-dimensions-right_C1W4L02.pptx [330.4K]
┃ ┃ ┣━━【4.4课件】_why-deep-representations_C1W4L03.pptx [1011.2K]
┃ ┃ ┣━━【4.5课件】_building-blocks-of-deep-neural-networks_C1W4L04.pptx [633.7K]
┃ ┃ ┣━━【4.6课件】_forward-and-backward-propagation_C1W4L06_ForwardBackProp_annotated.pdf [505.6K]
┃ ┃ ┣━━【4.7课件】_parameters-vs-hyperparameters_C1W4L05.pptx [251.2K]
┃ ┃ ┣━━【4.8课件】_what-does-this-have-to-do-with-the-brain_C1W4L06.pptx [3.3M]
┃ ┃ ┣━━4.1 深层神经网络.mkv [10.1M]
┃ ┃ ┣━━4.2 前向和反向传播.mkv [19.3M]
┃ ┃ ┣━━4.3 深层网络中的前向传播.mkv [12.7M]
┃ ┃ ┣━━4.4 核对矩阵的维数.mkv [16.9M]
┃ ┃ ┣━━4.5 为什么使用深层表示.mkv [17.1M]
┃ ┃ ┣━━4.6 搭建深层神经网络块.mkv [12.4M]
┃ ┃ ┣━━4.7 参数 VS 超参数.mkv [9.9M]
┃ ┃ ┣━━4.8 这和大脑有什么关系？.mkv [5.9M]
┃ ┃ ┗━━第四周quiz小测验.pdf [315.2K]
┃ ┗━━5.人工智能行业大师访谈 [324.4M]
┃ ┣━━1. 吴恩达采访 Geoffrey Hinton.mkv [190.7M]
┃ ┣━━2. 吴恩达采访 Pieter Abbeel.mkv [79.6M]
┃ ┗━━3. 吴恩达采访 Ian Goodfellow.mkv [54.1M]
┣━━02.改善深层神经网络：超参数调试、正则化以及优化 [766.9M]
┃ ┣━━1.第一周 深度学习的实用层面 [178.3M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【1.10课件】_vanishing-exploding-gradients_C2W1L07.pptx [301.9K]
┃ ┃ ┣━━【1.12课件】_numerical-approximation-of-gradients_C2W1L08.pptx [304.4K]
┃ ┃ ┣━━【1.13课件】_gradient-checking_C2W1L09.pptx [314K]
┃ ┃ ┣━━【1.1课件】_train-dev-test-sets_C2W1L01.pptx [281K]
┃ ┃ ┣━━【1.2课件】_bias-variance_C2W1L02.pptx [10.5M]
┃ ┃ ┣━━【1.3课件】_basic-recipe-for-machine-learning_C2W1L03_BasicRecipeML_annotated.pdf [223.4K]
┃ ┃ ┣━━【1.4课件】_regularization_C2W1L03.pptx [435.8K]
┃ ┃ ┣━━【1.5课件】_why-regularization-reduces-overfitting_C2W1L03b.pptx [227.7K]
┃ ┃ ┣━━【1.6课件】_dropout-regularization_C2W1L04.pptx [333.5K]
┃ ┃ ┣━━【1.8课件】_other-regularization-methods_C2W1L05.pptx [7M]
┃ ┃ ┣━━【1.9课件】_normalizing-inputs_C2W1L06.pptx [3.3M]
┃ ┃ ┣━━1.1 训练_开发_测试集.mkv [16.2M]
┃ ┃ ┣━━1.10 梯度消失与梯度爆炸.mkv [10.4M]
┃ ┃ ┣━━1.11 神经网络的权重初始化.mkv [9.8M]
┃ ┃ ┣━━1.12 梯度的数值逼近.mkv [10.3M]
┃ ┃ ┣━━1.13 梯度检验.mkv [9.4M]
┃ ┃ ┣━━1.14 关于梯度检验实现的注记.mkv [8.9M]
┃ ┃ ┣━━1.2 偏差_方差.mkv [13M]
┃ ┃ ┣━━1.3 机器学习基础.mkv [10M]
┃ ┃ ┣━━1.4 正则化.mkv [13.8M]
┃ ┃ ┣━━1.5 为什么正则化可以减少过拟合？.mkv [10.1M]
┃ ┃ ┣━━1.6 Dropout 正则化.mkv [12.6M]
┃ ┃ ┣━━1.7 理解 Dropout.mkv [10.5M]
┃ ┃ ┣━━1.8 其他正则化方法.mkv [11.4M]
┃ ┃ ┣━━1.9 归一化输入.mkv [8.6M]
┃ ┃ ┗━━第一周 quiz小测验.pdf [59.2K]
┃ ┣━━2.第二周 优化算法 [126.7M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【2.10课件】_the-problem-of-local-optima_C2W2L09.pptx [866.6K]
┃ ┃ ┣━━【2.1课件】_mini-batch-gradient-descent_C2W2L01.pptx [314K]
┃ ┃ ┣━━【2.2课件】_understanding-mini-batch-gradient-descent_C2W2L02.pptx [313.8K]
┃ ┃ ┣━━【2.3课件】_exponentially-weighted-averages_c2w2l03.pptx [680.8K]
┃ ┃ ┣━━【2.4课件】_understanding-exponentially-weighted-averages_C2W2L03b.pptx [621K]
┃ ┃ ┣━━【2.5课件】_bias-correction-in-exponentially-weighted-averages_C2W2L04.pptx [423K]
┃ ┃ ┣━━【2.6课件】_gradient-descent-with-momentum_C2W2L05.pptx [297.3K]
┃ ┃ ┣━━【2.7课件】_rmsprop_C2W2L06.pptx [180.8K]
┃ ┃ ┣━━【2.8课件】_adam-optimization-algorithm_C2W2L07.pptx [466K]
┃ ┃ ┣━━【2.9课件】_learning-rate-decay_C2W2L08.pptx [235.1K]
┃ ┃ ┣━━2.1 Mini-batch 梯度下降法.mkv [18.8M]
┃ ┃ ┣━━2.10 局部最优的问题.mkv [8.7M]
┃ ┃ ┣━━2.2 理解 mini-batch 梯度下降法.mkv [12.2M]
┃ ┃ ┣━━2.3 指数加权平均.mkv [9.4M]
┃ ┃ ┣━━2.4 理解指数加权平均.mkv [8.8M]
┃ ┃ ┣━━2.5 指数加权平均的偏差修正.mkv [8.9M]
┃ ┃ ┣━━2.6 动量梯度下降法.mkv [14.6M]
┃ ┃ ┣━━2.7 RMSprop.mkv [13.8M]
┃ ┃ ┣━━2.8 Adam 优化算法.mkv [12.7M]
┃ ┃ ┣━━2.9 学习率衰减.mkv [13.8M]
┃ ┃ ┗━━第二周 quiz小测验.pdf [771.4K]
┃ ┣━━3.第三周 超参数调试、Batch正则化和程序框架 [284.5M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【3.10课件】_deep-learning-frameworks_C2W3L08.pptx [88.1K]
┃ ┃ ┣━━【3.11课件】_tensorflow_C2W3L09.pptx [173.5K]
┃ ┃ ┣━━【3.1课件】_tuning-process_C2W3L01.pptx [204.3K]
┃ ┃ ┣━━【3.2课件】_using-an-appropriate-scale-to-pick-hyperparameters_C2W3L02.pptx [259.2K]
┃ ┃ ┣━━【3.3课件】_hyperparameters-tuning-in-practice-pandas-vs-caviar_C2W3L03.pptx [8.6M]
┃ ┃ ┣━━【3.4课件】_normalizing-activations-in-a-network_C2W3L04.pptx [274.5K]
┃ ┃ ┣━━【3.5课件】_fitting-batch-norm-into-a-neural-network_C2W3L05.pptx [388.1K]
┃ ┃ ┣━━【3.6课件】_why-does-batch-norm-work_C2W3L06.pptx [63.1M]
┃ ┃ ┣━━【3.7课件】_batch-norm-at-test-time_C2W3L07.pptx [205.6K]
┃ ┃ ┣━━【3.8课件】_softmax-regression_softmax-new.pptx [37.7M]
┃ ┃ ┣━━【3.9课件】_training-a-softmax-classifier_softmax2-new.pptx [90.6K]
┃ ┃ ┣━━3.1 调试处理.mkv [11.5M]
┃ ┃ ┣━━3.10 深度学习框架.mkv [9.8M]
┃ ┃ ┣━━3.11 TensorFlow.mkv [27M]
┃ ┃ ┣━━3.2 为超参数选择合适的范围.mkv [15.5M]
┃ ┃ ┣━━3.3 超参数训练的实践：Pandas VS Caviar.mkv [11.3M]
┃ ┃ ┣━━3.4 正则化网络的激活函数.mkv [15.2M]
┃ ┃ ┣━━3.5 将 Batch Norm 拟合进神经网络.mkv [20.2M]
┃ ┃ ┣━━3.6 Batch Norm 为什么奏效？.mkv [22.4M]
┃ ┃ ┣━━3.7 测试时的 Batch Norm.mkv [9.6M]
┃ ┃ ┣━━3.8 Softmax 回归.mkv [17M]
┃ ┃ ┣━━3.9 训练一个 Softmax 分类器.mkv [13.8M]
┃ ┃ ┗━━第三周 quiz 小测验.pdf [87.8K]
┃ ┗━━4.人工智能行业大师访谈 [177.4M]
┃ ┣━━1. 吴恩达采访 Yoshua Bengio.mkv [113.5M]
┃ ┗━━2. 吴恩达采访 林元庆.mkv [63.9M]
┣━━03.结构化机器学习项目 [652.6M]
┃ ┣━━1.第一周 机器学习（ML）策略（1） [187.4M]
┃ ┃ ┣━━【1.10课件】_understanding-human-level-performance_C3W1L09.pptx [5.4M]
┃ ┃ ┣━━【1.10课件】_understanding-human-level-performance_Understanding_human_level_performance.pdf [366.6K]
┃ ┃ ┣━━【1.11课件】_surpassing-human-level-performance_C3W1L10.pptx [190.2K]
┃ ┃ ┣━━【1.11课件】_surpassing-human-level-performance_Surpassing_human_level_performance.pdf [358.9K]
┃ ┃ ┣━━【1.12课件】_improving-your-model-performance_C3W1L11.pptx [148.6K]
┃ ┃ ┣━━【1.12课件】_improving-your-model-performance_Improving_your_model_performance.pdf [348.6K]
┃ ┃ ┣━━【1.1课件】_why-ml-strategy_C3W1L01.pptx [25.1M]
┃ ┃ ┣━━【1.2课件】_orthogonalization_C3W1L02.pptx [8.4M]
┃ ┃ ┣━━【1.2课件】_orthogonalization_Orthogonalization.pdf [324.4K]
┃ ┃ ┣━━【1.3课件】_single-number-evaluation-metric_C3W1L03.pptx [223.9K]
┃ ┃ ┣━━【1.3课件】_single-number-evaluation-metric_Single_number_evaluation_metric-2.pdf [464.1K]
┃ ┃ ┣━━【1.4课件】_satisficing-and-optimizing-metric_C3W1L04.pptx [192.7K]
┃ ┃ ┣━━【1.4课件】_satisficing-and-optimizing-metric_Satisficing_and_optimizing_metric.pdf [452.1K]
┃ ┃ ┣━━【1.5课件】_train-dev-test-distributions_C3W1L05.pptx [894.3K]
┃ ┃ ┣━━【1.5课件】_train-dev-test-distributions_Training_development_and_test_distributions.pdf [316.6K]
┃ ┃ ┣━━【1.6课件】_size-of-the-dev-and-test-sets_C3W1L06.pptx [215.2K]
┃ ┃ ┣━━【1.6课件】_size-of-the-dev-and-test-sets_Size_of_the_development_and_test_sets.pdf [362.8K]
┃ ┃ ┣━━【1.7课件】_when-to-change-dev-test-sets-and-metrics_C3W1L07.pptx [13.6M]
┃ ┃ ┣━━【1.7课件】_when-to-change-dev-test-sets-and-metrics_When_to_change_develpment_test_sets_and_metrics.pdf [457.7K]
┃ ┃ ┣━━【1.8课件】_why-human-level-performance_C3W1L08.pptx [146.6K]
┃ ┃ ┣━━【1.8课件】_why-human-level-performance_Why_human_level_performance.pdf [350.7K]
┃ ┃ ┣━━【1.9课件】_avoidable-bias_Avoidable_bias.pdf [337.3K]
┃ ┃ ┣━━【1.9课件】_avoidable-bias_C3W1L08B.pptx [10.4M]
┃ ┃ ┣━━1.1 为什么是 ML 策略.mkv [8.1M]
┃ ┃ ┣━━1.10 理解人的表现.mkv [1000.6K]
┃ ┃ ┣━━1.11 超过人的表现.mkv [10.1M]
┃ ┃ ┣━━1.12 改善你的模型的表现.mkv [11M]
┃ ┃ ┣━━1.2 正交化.mkv [9.8M]
┃ ┃ ┣━━1.3 单一数字评估指标.mkv [13.3M]
┃ ┃ ┣━━1.4 满足和优化指标.mkv [9.6M]
┃ ┃ ┣━━1.5 训练_开发_测试集划分.mkv [10.7M]
┃ ┃ ┣━━1.6 开发集合测试集的大小.mkv [11M]
┃ ┃ ┣━━1.7 什么时候该改变开发_测试集和指标.mkv [11.5M]
┃ ┃ ┣━━1.8 为什么是人的表现.mkv [10.7M]
┃ ┃ ┣━━1.9 可避免偏差.mkv [10.9M]
┃ ┃ ┗━━第一周 quiz 小测验.pdf [623.5K]
┃ ┣━━2.第二周 机器学习（ML）策略（2） [278.5M]
┃ ┃ ┣━━【2.10课件】_whether-to-use-end-to-end-deep-learning_C3W2L10.pptx [5.4M]
┃ ┃ ┣━━【2.10课件】_whether-to-use-end-to-end-deep-learning_Whether_to_use_end_to_end_deep_learning.pdf [357.9K]
┃ ┃ ┣━━【2.1课件】_carrying-out-error-analysis_C3W2L01.pptx [1M]
┃ ┃ ┣━━【2.2课件】_cleaning-up-incorrectly-labeled-data_C3W2L02.pptx [32.4M]
┃ ┃ ┣━━【2.3课件】_build-your-first-system-quickly-then-iterate_Build_System_Quickly.pdf [328.4K]
┃ ┃ ┣━━【2.3课件】_build-your-first-system-quickly-then-iterate_C3W2L03.pptx [116K]
┃ ┃ ┣━━【2.4课件】_training-and-testing-on-different-distributions_C3W2L04.pptx [13.1M]
┃ ┃ ┣━━【2.4课件】_training-and-testing-on-different-distributions_Training_and_testing_on_different_distributions.pdf [342.1K]
┃ ┃ ┣━━【2.5课件】_bias-and-variance-with-mismatched-data-distributions_Bias_and_variance_with_mismatched_data_distributions.pdf [359.6K]
┃ ┃ ┣━━【2.5课件】_bias-and-variance-with-mismatched-data-distributions_C3W2L05.pptx [357.3K]
┃ ┃ ┣━━【2.6课件】_addressing-data-mismatch_Adressing_data_mismatch.pdf [330.1K]
┃ ┃ ┣━━【2.6课件】_addressing-data-mismatch_C3W2L06.pptx [9.3M]
┃ ┃ ┣━━【2.7课件】_transfer-learning_C3W2L07.pptx [218.4K]
┃ ┃ ┣━━【2.7课件】_transfer-learning_Transfer_Learning.pdf [505.4K]
┃ ┃ ┣━━【2.8课件】_multi-task-learning_C3W2L08.pptx [1.5M]
┃ ┃ ┣━━【2.8课件】_multi-task-learning_Multi_Task_Learning.pdf [564.9K]
┃ ┃ ┣━━【2.9课件】_what-is-end-to-end-deep-learning_C3W2L09.pptx [5.7M]
┃ ┃ ┣━━【2.9课件】_what-is-end-to-end-deep-learning_What_is_end_to_end_deep_learning.pdf [330.3K]
┃ ┃ ┣━━2.1 进行误差分析.mkv [18.5M]
┃ ┃ ┣━━2.10 是否要使用端到端的深度学习.mkv [17.1M]
┃ ┃ ┣━━2.2 清除标注错误的数据.mkv [26.1M]
┃ ┃ ┣━━2.3 快速搭建你的第一个系统，并进行迭代.mkv [11.9M]
┃ ┃ ┣━━2.4 在不同的划分上进行训练并测试.mkv [18.4M]
┃ ┃ ┣━━2.5 不匹配数据划分的偏差和方差.mkv [27.2M]
┃ ┃ ┣━━2.6 定位数据不匹配.mkv [17.5M]
┃ ┃ ┣━━2.7 迁移学习.mkv [21.7M]
┃ ┃ ┣━━2.8 多任务学习.mkv [28.4M]
┃ ┃ ┣━━2.9 什么是端到端的深度学习.mkv [18.5M]
┃ ┃ ┗━━第二周quiz小测验.pdf [955.6K]
┃ ┗━━3.人工智能行业大师访谈 [186.7M]
┃ ┣━━1. 采访 Andrej Karpathy.mkv [83.6M]
┃ ┗━━2. 采访 Ruslan Salakhutdinov.mkv [103.1M]
┣━━04.卷积神经网络 [1G]
┃ ┣━━第二周 深度卷积网络：实例探究 [165.6M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【2.1课件】_why-look-at-case-studies_C4W2L01_WhyLookAtCaseStudies.pptx [90.6K]
┃ ┃ ┣━━【2.2课件】_classic-networks_C4W2L02_ClassicNetworks.pptx [336.1K]
┃ ┃ ┣━━【2.3课件】_resnets_C4W2L03_ResNets.pptx [185.6K]
┃ ┃ ┣━━【2.5课件】_networks-in-networks-and-1x1-convolutions_C4W2L05_NetworkinNetworkand1x1.pptx [218.9K]
┃ ┃ ┣━━【2.6课件】_inception-network-motivation_C4W2L06_InceptionNetworkMotivation.pptx [229.8K]
┃ ┃ ┣━━【2.7课件】_inception-network_C4W2L07_InceptionNetwork.pptx [4M]
┃ ┃ ┣━━2.1 为什么要进行实例探究？.mkv [7.7M]
┃ ┃ ┣━━2.10 数据扩充.mkv [16.1M]
┃ ┃ ┣━━2.11 计算机视觉现状.mkv [18M]
┃ ┃ ┣━━2.2 经典网络.mkv [25.9M]
┃ ┃ ┣━━2.3 残差网络.mkv [10.8M]
┃ ┃ ┣━━2.4 残差网络为什么有用？.mkv [14.5M]
┃ ┃ ┣━━2.5 网络中的网络以及 1×1 卷积.mkv [9.6M]
┃ ┃ ┣━━2.6 谷歌 Inception 网络简介.mkv [15.3M]
┃ ┃ ┣━━2.7 Inception 网络.mkv [14.4M]
┃ ┃ ┣━━2.8 使用开源的实现方案.mkv [13.3M]
┃ ┃ ┣━━2.9 迁移学习.mkv [15M]
┃ ┃ ┗━━第二周quiz小测验.pdf [57.6K]
┃ ┣━━第三周 目标检测 [231M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【3.10课件】_optional-region-proposals_C4W3L10_RegionProposals.pptx [825.2K]
┃ ┃ ┣━━【3.1课件】_object-localization_C4W3L01_ObjectLocalization.pptx [8.6M]
┃ ┃ ┣━━【3.2课件】_landmark-detection_C4W3L02_LandmarkDetection.pptx [20.2M]
┃ ┃ ┣━━【3.3课件】_object-detection_C4W3L03_ObjectDetection.pptx [23.8M]
┃ ┃ ┣━━【3.4课件】_convolutional-implementation-of-sliding-windows_C4W3L04_ConvImpSlidingWindows.pptx [17.6M]
┃ ┃ ┣━━【3.6课件】_intersection-over-union_C4W3L06_InstersecOverUnion.pptx [4.3M]
┃ ┃ ┣━━【3.7课件】_non-max-suppression_C4W3L07_NonmaxSuppression.pptx [1.9M]
┃ ┃ ┣━━【3.8课件】_anchor-boxes_C4W3L08_AnchorBoxes.pptx [5.8M]
┃ ┃ ┣━━【3.9课件】_yolo-algorithm_C4W3L09_YOLOAlgorithm.pptx [5M]
┃ ┃ ┣━━3.1 目标定位.mkv [18.9M]
┃ ┃ ┣━━3.10 候选区域.mkv [11.9M]
┃ ┃ ┣━━3.2 特征点检测.mkv [11.4M]
┃ ┃ ┣━━3.3 目标检测.mkv [9.1M]
┃ ┃ ┣━━3.4 卷积的滑动窗口实现.mkv [17.2M]
┃ ┃ ┣━━3.5 Bounding Box预测.mkv [25.2M]
┃ ┃ ┣━━3.6 交并比.mkv [7M]
┃ ┃ ┣━━3.7 非极大值抑制.mkv [12.1M]
┃ ┃ ┣━━3.8 Anchor Boxes.mkv [18.2M]
┃ ┃ ┣━━3.9 YOLO 算法.mkv [11.2M]
┃ ┃ ┗━━第三周quiz小测验.pdf [1.1M]
┃ ┣━━第四周 特殊应用：人脸识别和神经风格转换 [495M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━【4.10课件】_style-cost-function_C4W4L10_StyleCostFunction.pptx [624.7K]
┃ ┃ ┣━━【4.11课件】_1d-and-3d-generalizations_C4W4L11_1D3DGeneralizations.pptx [3M]
┃ ┃ ┣━━【4.1课件】_what-is-face-recognition_C4W4L01_WhatIsFaceRecog.pptx [137.7M]
┃ ┃ ┣━━【4.2课件】_one-shot-learning_C4W4L02_OneShotLearning.pptx [32.2M]
┃ ┃ ┣━━【4.3课件】_siamese-network_C4W4L03_SiameseNetwork.pptx [8.6M]
┃ ┃ ┣━━【4.4课件】_triplet-loss_C4W4L04_TripletLoss.pptx [59.5M]
┃ ┃ ┣━━【4.5课件】_face-verification-and-binary-classification_C4W4L05_FaceVerifABinaryClass.pptx [29.2M]
┃ ┃ ┣━━【4.6课件】_what-is-neural-style-transfer_C4W4L06_WhatIsNeuralTransferStyle.pptx [38.1M]
┃ ┃ ┣━━【4.7课件】_what-are-deep-convnets-learning_C4W4L07_WhatAreDeepCNsLearning.pptx [21.2M]
┃ ┃ ┣━━【4.8课件】_cost-function_C4W4L08_CostFunction.pptx [34.2M]
┃ ┃ ┣━━【4.9课件】_content-cost-function_C4W4L09_ContentCostFunction.pptx [177.4K]
┃ ┃ ┣━━4.1 什么是人脸识别？.mkv [14.1M]
┃ ┃ ┣━━4.10 风格代价函数.mkv [23.4M]
┃ ┃ ┣━━4.11 一维到三维推广.mkv [13.7M]
┃ ┃ ┣━━4.2 One-Shot 学习.mkv [7.2M]
┃ ┃ ┣━━4.3 Siamese 网络.mkv [7.3M]
┃ ┃ ┣━━4.4 Triplet 损失.mkv [24.3M]
┃ ┃ ┣━━4.5 面部验证与二分类.mkv [9.5M]
┃ ┃ ┣━━4.6 什么是神经风格转换？.mkv [4.5M]
┃ ┃ ┣━━4.7 什么是深度卷积网络？.mkv [14.4M]
┃ ┃ ┣━━4.8 代价函数.mkv [6.5M]
┃ ┃ ┣━━4.9 内容代价函数.mkv [5.5M]
┃ ┃ ┗━━第四周quiz小测验.pdf [103.3K]
┃ ┗━━第一周 卷积神经网络 [179.4M]
┃ ┣━━编程作业
┃ ┣━━【1.10课件】_cnn-example_C4W1L10_CNNExample.pptx [179.1K]
┃ ┣━━【1.11课件】_why-convolutions_C4W1L11_WhyConvs.pptx [4.5M]
┃ ┣━━【1.1课件】_computer-vision_C4W1L01_ComputerVision.pptx [5.8M]
┃ ┣━━【1.2课件】_edge-detection-example_C4W1L02_EdgeDetectionExample.pptx [6.6M]
┃ ┣━━【1.3课件】_more-edge-detection_C4W1L03_MoreEdgeDetection.pptx [189.5K]
┃ ┣━━【1.4课件】_padding_C4W1L04_Padding.pptx [199.5K]
┃ ┣━━【1.5课件】_strided-convolutions_C4W1L05_StridedConv.pptx [201.3K]
┃ ┣━━【1.6课件】_convolutions-over-volume_C4W1L06_ConvolutionsOverVolumes.pptx [223.4K]
┃ ┣━━【1.7课件】_simple-convolutional-network-example_C4W1L08_SimpleCNNExample.pptx [174.2K]
┃ ┣━━【1.9课件】_pooling-layers_C4W1L09_PoolingLayers.pptx [156.6K]
┃ ┣━━1.1 计算机视觉.mkv [10.6M]
┃ ┣━━1.10 卷积神经网络示例.mkv [19.2M]
┃ ┣━━1.11 为什么使用卷积？.mkv [15.4M]
┃ ┣━━1.2 边缘检测示例.mkv [15.4M]
┃ ┣━━1.3 更多边缘检测内容.mkv [11.8M]
┃ ┣━━1.4 Padding.mkv [13.6M]
┃ ┣━━1.5 卷积步长.mkv [12.4M]
┃ ┣━━1.6 三维卷积.mkv [14.3M]
┃ ┣━━1.7 单层卷积网络.mkv [23.1M]
┃ ┣━━1.8 简单卷积网络示例.mkv [12M]
┃ ┣━━1.9 池化层.mkv [13.3M]
┃ ┗━━第一周quiz小测验.pdf [58.4K]
┣━━05.序列模型 [489.3M]
┃ ┣━━第二周 自然语言处理与词嵌入 [146.3M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━2.1 词汇表征.mkv [14.8M]
┃ ┃ ┣━━2.10 词嵌入除偏.mkv [16.2M]
┃ ┃ ┣━━2.2 使用词嵌入.mkv [13.4M]
┃ ┃ ┣━━2.3 词嵌入的特性.mkv [16.9M]
┃ ┃ ┣━━2.4 嵌入矩阵.mkv [8.5M]
┃ ┃ ┣━━2.5 学习词嵌入.mkv [15.6M]
┃ ┃ ┣━━2.6 Word2Vec.mkv [17.9M]
┃ ┃ ┣━━2.7 负采样.mkv [17.4M]
┃ ┃ ┣━━2.8 GloVe 词向量.mkv [15.1M]
┃ ┃ ┣━━2.9 情绪分类.mkv [10.6M]
┃ ┃ ┗━━第二周quiz小测验.pdf [74.1K]
┃ ┣━━第三周 序列模型和注意力机制 [157.4M]
┃ ┃ ┣━━编程作业
┃ ┃ ┣━━3.1 基础模型.mkv [10.3M]
┃ ┃ ┣━━3.10 触发字检测.mkv [8.5M]
┃ ┃ ┣━━3.11 结论和致谢.mkv [5.1M]
┃ ┃ ┣━━3.2 选择最可能的句子.mkv [13.7M]
┃ ┃ ┣━━3.3 定向搜索.mkv [17.4M]
┃ ┃ ┣━━3.4 改进定向搜索.mkv [15.1M]
┃ ┃ ┣━━3.5 定向搜索的误差分析.mkv [14.6M]
┃ ┃ ┣━━3.6 Bleu 得分（选修）.mkv [27.5M]
┃ ┃ ┣━━3.7 注意力模型直观理解.mkv [14.3M]
┃ ┃ ┣━━3.8 注意力模型.mkv [17.8M]
┃ ┃ ┣━━3.9 语音辨识.mkv [13M]
┃ ┃ ┗━━第三周quiz小测验.pdf [136.8K]
┃ ┗━━第一周 循环序列模型 [185.6M]
┃ ┣━━编程作业
┃ ┣━━【1.1课件】_why-sequence-models_C5W1L01_WhySequenceModels.pptx [10.8M]
┃ ┣━━【1.2课件】_notation_C5W1L02_Notation.pptx [173.9K]
┃ ┣━━1.10 长短期记忆（LSTM）.mkv [18.4M]
┃ ┣━━1.11 双向神经网络.mkv [13.9M]
┃ ┣━━1.12 深层循环神经网络.mkv [8.4M]
┃ ┣━━1.1为什么选择序列模型.mkv [5.1M]
┃ ┣━━1.2数学符号.mkv [12.6M]
┃ ┣━━1.3循环神经网络.mkv [22.6M]
┃ ┣━━1.4通过时间的方向传播.mkv [10.4M]
┃ ┣━━1.5不同类型的循环神经网络.mkv [14.5M]
┃ ┣━━1.6 语言模型和序列生成.mkv [17.1M]
┃ ┣━━1.7 对新序列采样.mkv [13.3M]
┃ ┣━━1.8带有神经网络的梯度消失.mkv [11.7M]
┃ ┣━━1.9 GRU 单元.mkv [26.2M]
┃ ┗━━第一周quiz小测验.pdf [363.3K]
┣━━地址.txt [58B]
┗━━如何打开jpynb作业文件.pdf [443.6K]
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发表于 2021-1-7 17:10:35
发表于 2021-2-7 21:06:43
发表于 2021-3-21 13:14:13
发表于 2021-4-26 08:55:37
