728x90 반응형 전체 글88 Neural Networks and Deep Learning (3) ▤ 목차Neural Networks BasicsLogistic Regression as a Neural NetworkComputation GraphA computation graph is an organized forward pass (propagation) to compute the function of a neural network, followed by a backward pass (propagation) to calculate the gradients of a neural network.This is like looking at a math formula and calculating step by step to get the answer. One step of ________ propagation.. 2024. 11. 14. Neural Networks and Deep Learning (2) ▤ 목차Neural Networks BasicsSet up a machine learning problem with a neural network mindset and use vectorization to speed up your models.Learning ObjectivesBuild a logistic regression model structured as a shallow neural networkBuild the general architecture of a learning algorithm, including parameter initialization, cost function, gradient calculation, and optimization implementation (gradient .. 2024. 11. 13. Neural Networks and Deep Learning (1) ▤ 목차Introduction to Deep LearningQuizQ1What does the analogy “AI is the new electricity” refer to?AI is powering personal devices in our homes and offices, similar to electricity.Like electricity started about 100 years ago, AI is transforming multiple industries.Through the “smart grid”, AI is delivering a new wave of electricity.AI runs on computers and is thus powered by electricity, but it i.. 2024. 11. 12. Neural Networks and Deep Learning (0) ▤ 목차About this CourseIn the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key paramete.. 2024. 11. 11. Deep Learning Specialization ▤ 목차What you'll learnBuild and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applicationsTrain test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlowBuild a CNN and apply it to detection and recognition tasks, use neural style transf.. 2024. 11. 10. Probability & Statistics for Machine Learning & Data Science (25) ▤ 목차Confidence Intervals and Hypothesis testingHypothesis Testingt-DistributionWhen the data can be modeled as a Gaussian distribution with parameters μ and σ2, the sample mean will also follow a Gaussian distribution of the same mean, but with a smaller standard deviation.If we don’t know the standard deviation, then we use T-statistics instead of Z-statistics.But using T-statistics.. 2024. 11. 8. 이전 1 2 3 4 5 6 7 8 ··· 15 다음 728x90 반응형
