728x90 반응형 전체 글88 Marketing Strategy Proposal with Instacart Data Analysis (0) ▤ 목차OverviewThis report provides a comprehensive analysis of customer behavior and purchasing patterns within a specific e-commerce platform. The primary objective was to analyze customer data, identify high-value customers (VIPs), and develop strategies to increase revenue through targeted promotions and improved product offerings. Key findings include the identification of VIPs who significant.. 2024. 12. 23. Backpropagation ▤ 목차The Purpose of Backpropagation인공 신경망을 학습시키기 위한 알고리즘 중 하나 신경망을 학습시키는 목표 중 하나는 도출된 예측값과 실제 값의 차이(오차)를 줄이기 위함이다 그렇기에 역전파를 사용해 오차를 모든 가중치에 전달하여 갱신을 하며 궁극적으로 오차를 줄이는 기법이다노드가 가지고 있는 가중치(weight)나 편향(bias) 같은 변수들을 어떻게 갱신(update) 하나?노드의 변수들을 어떻게 개별적으로 얼마큼 업데이트 하나?Chain rule(연쇄 법칙)을 이용해 위 두 가지 질문들을 해결할 수 있다Chain Rule (연쇄법칙)💡 Chain rule (연쇄법칙) 함수 $f, g$가 있을 때 $f$와 $g$가 모두 미분 가능하고 $F=f(g(x))=f \circ g$.. 2024. 12. 17. 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. 이전 1 2 3 4 5 6 ··· 15 다음 728x90 반응형
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