Coursera 썸네일형 리스트형 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (1) Practical 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 from 1.. 더보기 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (0) About 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 techn.. 더보기 Neural Networks and Deep Learning (11) Deep 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. True/F.. 더보기 Neural Networks and Deep Learning (10) Deep 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 something we.. 더보기 Neural Networks and Deep Learning (9) Deep 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 to b.. 더보기 Neural Networks and Deep Learning (8) Shallow 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 examp.. 더보기 Neural Networks and Deep Learning (7) Shallow Neural NetworksShallow Neural NetworkActivation FunctionsSo far we’ve been using sigmoid as our activation function, but we can use other activation functions such as hyperbolic tangent, aka tanh.Tanh falls within the interval between -1 and 1 and, when plotted, looks like a shifted sigmoid function.Using tanh can center your data to have a mean of 0 rather than 0.5 when using a sigmoid .. 더보기 Neural Networks and Deep Learning (6) Shallow Neural NetworksBuild a neural network with one hidden layer, using forward propagation and backpropagation.Learning ObjectivesDescribe hidden units and hidden layersUse units with a non-linear activation function, such as tanhImplement forward and backward propagationApply random initialization to your neural networkIncrease fluency in Deep Learning notations and Neural Network Represent.. 더보기 이전 1 2 3 4 ··· 9 다음
