728x90 반응형 신경망15 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (1) ▤ 목차Practical Aspects of Deep LearningRegularizing your Neural NetworkClarification about Upcoming Regularization VideoPlease note that in the next video (Regularization) at 5:45, the Frobenius norm formula should be the following:$∣∣w^{[l]}∣∣^2=∑_{i=1}^{n^{[l]}}∑_{j=1}^{n^{[l−1]}}(w_{i,j}^{[l]})^2$ The limit of summation of i should be from 1 to $n^{[l]}$, The limit of summation of j should be .. 2024. 12. 10. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (0) ▤ 목차About this CourseIn the second course of the Deep Learning Specialization, you will open the deep learning black box to systematically understand the processes that drive performance and generate good results. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network t.. 2024. 12. 5. Neural Networks and Deep Learning (11) ▤ 목차Deep Neural NetworksQuizQ1What is stored in the 'cache' during forward propagation for later use in backward propagation?$W^{[l]}$$Z^{[l]}$$b^{[l]}$$A^{[l]}$Answer더보기2Yes. This value is useful in the calculation of $dW^{[l]}$ in the backward propagation.Q2We use the “cache” in implementing forward and backward propagation to pass useful values to the next layer in the forward propagation. Tr.. 2024. 12. 4. Neural Networks and Deep Learning (10) ▤ 목차Deep Neural NetworksDeep Neural NetworkForward and Backward PropagationOptional Reading: Feedforward Neural Networks in DepthFeedforward Neural Networks in Depth - Deep Learning Specialization / Deep Learning Resources - DeepLearning.AIParameters vs HyperparametersParameters are the weights and biases, something deep neural networks optimize to get close to the answer.Parameters are somethin.. 2024. 11. 28. Neural Networks and Deep Learning (9) ▤ 목차Deep Neural NetworksAnalyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.Learning ObjectivesDescribe the successive block structure of a deep neural networkBuild a deep L-layer neural networkAnalyze matrix and vector dimensions to check neural network implementationsUse a cache to pass information from forward .. 2024. 11. 27. Neural Networks and Deep Learning (8) ▤ 목차Shallow Neural NetworksQuizQ1Which of the following is true? (Check all that apply.)$a^{[2]}$ denotes the activation vector of the 2nd layer.$a^{[2](12)}$ denotes the activation vector of the 2nd layer for the 12th training example.$a^{[2](12)}$ denotes activation vector of the 12th layer on the 2nd training example.$a^{[2]}_4$ is the activation output of the 2nd layer for the 4th training e.. 2024. 11. 26. 이전 1 2 3 다음 728x90 반응형
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