machine learning 썸네일형 리스트형 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 Neural Networks and Deep Learning (5) Neural Networks BasicsQuizQ1In logistic regression, given $\bf x$ and parameters $w ∈ {\mathbb R^{n_x}}, b∈{\mathbb R}$. Which of the following best expresses what we want $\hat y$ to tell us?Choice$\sigma(W \textbf{x} + b)$$P(y = \hat y | x)$$\sigma(W \bf x)$$P(y = 1|\bf x)$Hint더보기Remember that we are interested in the probability that $y=1$.Answer더보기4Yes. We want the output $\hat y$ to tell us.. 더보기 Neural Networks and Deep Learning (4) Neural Networks BasicsPython and VectorizationVectorizationVectorization is how to apply a function in parallel across all examples simultaneously.In Python, vectorization avoids using the for-loop and reduces the computation and time. True or false. Vectorization cannot be done without a GPU.더보기FalseMore Vectorization ExamplesWe can apply any function to matrices or vectors.In our example, we a.. 더보기 Neural Networks and Deep Learning (3) Neural 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 on .. 더보기 Neural Networks and Deep Learning (2) Neural 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 desc.. 더보기 Neural Networks and Deep Learning (1) Introduction 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 is le.. 더보기 Neural Networks and Deep Learning (0) About 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 parameters i.. 더보기 Deep Learning Specialization What 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 transfer t.. 더보기 이전 1 2 3 4 5 ··· 10 다음
