# lab1:
In this ungraded lab, you can see how a linear regression model is defined in code, and you can see plots that show how well a model fits some data given choices of w and b. You can also try different values of w and b to see if it improves the fit to the data.
# lab2
This optional lab will show you how the cost function is implemented in code. And given a small training set and different choices for the parameters you’ll be able to see how the cost varies depending on how well the model fits the data.
In the optional lab, you'll also get to play with an interactive contour plot. You can use your mouse cursor to click anywhere on the contour plot, and you see the straight line defined by the values you chose, for parameters w and b.
Finally the optional lab also has a 3d surface plot that you can manually rotate and spin around, using your mouse cursor, to take a better look at what the cost function looks like.
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2022-Machine-Learning-Specialization-main.zip 吴恩达机器学习ppt
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ipynb:122个
py:70个
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2024-01-28
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吴恩达的机器学习课程主要包括两门,一门是在Cousera上的《机器学习》,另一门是他在斯坦福大学教授的《CS229: Machine Learning》。 Cousera上的《机器学习》课程侧重于概念理解,而不是数学推导。这门课程重视联系实际和经验总结,吴恩达老师列举了许多算法实际应用的例子,并分享了他们入门AI时面临的问题以及处理这些难题的经验。这门课程适合初学者,课程内容可以在Cousera网站上在线观看,需要注册后可申请免费观看。 斯坦福大学的《CS229: Machine Learning》课程则更加偏好理论,适合于有一定数学基础的同学学习。这是吴恩达在斯坦福的机器学习课程,历史悠久,仍然是最经典的机器学习课程之一。 机器学习是一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。它专门研究计算机怎样模拟或实现人类的学习行为,以获取新的知识或技能,重新组织已有的知识结构使之不断改善自身的性能。它是人工智能的核心,是使计算机具有智能的根本途径。 如需更多吴恩达机器学习课程相关内容,可以登录Coursera官网和B站查看课程介绍。
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2022-Machine-Learning-Specialization-main.zip
吴恩达机器学习ppt (524个子文件)
C2_W1_Assign1_Broadcasting.gif 10.42MB
C1_W2_Lab04_dot_notrans.gif 1.61MB
C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb 886KB
C2_W1_Lab02_CoffeeRoasting_TF.ipynb 538KB
C2_W1_Lab02_CoffeeRoasting_TF.ipynb 538KB
C2_W1_Lab02_CoffeeRoasting_TF.ipynb 373KB
C1_W3_Lab04_LogisticLoss_Soln.ipynb 315KB
C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb 294KB
C1_W3_Lab06_One_Vs_All_Soln.ipynb 272KB
C1_W3_Lab06_One_Vs_All_user.ipynb 272KB
C1_W3_Lab06_Gradient_Descent_Soln.ipynb 166KB
C1_W3_Lab07_Overfitting_Soln.ipynb 152KB
C1_W3_Lab07_Overfitting_user.ipynb 152KB
C1_W3_Lab07_Overfitting_Soln.ipynb 152KB
C1_W3_Lab07_Overfitting_user.ipynb 152KB
C1_W3_Lab09_Regularization_Soln.ipynb 114KB
C2_W1_Lab01_Neurons_and_Layers.ipynb 111KB
C2_W1_Lab01_Neurons_and_Layers.ipynb 111KB
C1_W3_Logistic_Regression.ipynb 66KB
C1_W3_Lab08_Overfitting_Soln.ipynb 62KB
C1_W2_Lab05_Sklearn_GD_Soln.ipynb 51KB
C2_W2_Relu-checkpoint.ipynb 48KB
C2_W2_Relu.ipynb 48KB
C1_W3_Lab01_Classification_Soln.ipynb 48KB
C1_W3_Lab02_Sigmoid_function_Soln.ipynb 46KB
C2_W3_Assignment.ipynb 46KB
C2_W3_Assignment.ipynb 46KB
C1_W3_Lab03_Decision_Boundary_Soln.ipynb 45KB
C1_W3_Lab08_Overfitting_Soln.ipynb 43KB
C2_W1_Assignment.ipynb 41KB
C1_W3_Lab03_Cost_Function_Soln.ipynb 37KB
C1_W3_Lab03_Cost_Function_user.ipynb 36KB
C1_W3_Lab03_Cost_Function_Soln.ipynb 35KB
C1_W3_Lab03_Cost_Function_user.ipynb 35KB
C1_W2_Linear_Regression.ipynb 34KB
C2_W2_Multiclass_TF.ipynb 34KB
C2_W2_Multiclass_TF.ipynb 34KB
C1_W3_Lab02_Decision_Boundary_Soln.ipynb 31KB
C1_W3_Lab02_Decision_Boundary_user.ipynb 31KB
C1_W3_Lab06_One_Vs_All_Soln.ipynb 30KB
C1_W3_Lab06_One_Vs_All_user.ipynb 30KB
C2_W2_Relu.ipynb 29KB
C2_W2_Assignment.ipynb 27KB
C2_W2_SoftMax-Copy1.ipynb 26KB
C2_W2_SoftMax-Copy1.ipynb 26KB
C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb 25KB
C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb 25KB
C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb 25KB
C1_W2_Lab02_Multiple_Variable_Soln.ipynb 24KB
C1_W2_Lab02_Multiple_Variable_Soln.ipynb 24KB
C2_W2_SoftMax.ipynb 21KB
C2_W2_SoftMax.ipynb 21KB
C1_W1_Lab05_Gradient_Descent_Soln.ipynb 21KB
C1_W1_Lab05_Gradient_Descent_Soln.ipynb 20KB
C1_W3_Lab01_Sigmoid_function_Soln.ipynb 20KB
C1_W3_Lab01_Sigmoid_function_user.ipynb 20KB
C1_W1_Lab05_Gradient_Descent_Soln.ipynb 19KB
C1_W3_Lab01_Sigmoid_function_Soln.ipynb 19KB
C1_W3_Lab01_Sigmoid_function_user.ipynb 19KB
C1_W3_Lab09_Regularization_Soln.ipynb 18KB
C1_W3_Lab04_Gradient_Descent_Soln.ipynb 16KB
C1_W3_Lab06_Gradient_Descent_Soln.ipynb 16KB
C1_W3_Lab04_Gradient_Descent_user.ipynb 16KB
C1_W3_Lab06_Gradient_Descent_Soln.ipynb 15KB
C1_W3_Lab04_Gradient_Descent_Soln.ipynb 14KB
C1_W3_Lab04_Gradient_Descent_user.ipynb 14KB
C1_W1_Lab03_Model_Representation_Soln.ipynb 13KB
C1_W1_Lab03_Model_Representation_Soln.ipynb 13KB
C1_W1_Lab03_Model_Representation_Soln.ipynb 13KB
C1_W3_Lab09_Regularized_Gradient_Descent_Soln.ipynb 12KB
C1_W3_Lab09_Regularized_Gradient_Descent_user.ipynb 12KB
C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb 12KB
C2_W1_Lab01_Neurons_and_Layers.ipynb 12KB
C1_W3_Lab03_Cost_Function.ipynb 12KB
C1_W3_Lab04_LogisticLoss.ipynb 11KB
C1_W3_Lab05_Cost_Function_Soln.ipynb 10KB
C1_W3_Lab04_Gradient_Descent.ipynb 10KB
C1_W3_Lab02_Sigmoid_function_Soln.ipynb 10KB
C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb 10KB
C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb 10KB
C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb 10KB
C1_W1_Lab04_Cost_function_Soln.ipynb 10KB
C1_W1_Lab04_Cost_function_Soln.ipynb 10KB
C1_W1_Lab04_Cost_function_Soln.ipynb 10KB
C1_W3_Lab04_LogisticLoss_Soln.ipynb 9KB
C1_W3_Lab03_Decision_Boundary_Soln.ipynb 9KB
C1_W3_Lab08_Regularized_Cost_Soln.ipynb 9KB
C1_W3_Lab05_Cost_Function_Soln.ipynb 8KB
C1_W3_Lab05_Cost_Function_Soln.ipynb 8KB
C1_W3_Lab02_Sigmoid_function_Soln.ipynb 8KB
C1_W2_Lab06_Sklearn_Normal_Soln.ipynb 8KB
C1_W2_Lab06_Sklearn_Normal_Soln.ipynb 8KB
C1_W3_Lab03_Decision_Boundary_Soln.ipynb 8KB
C1_W3_Lab08_Regularized_Cost_Soln.ipynb 8KB
C1_W3_Lab08_Regularized_Cost_user.ipynb 8KB
C1_W3_Lab09_Regularized_Cost_Soln.ipynb 8KB
C1_W3_Lab08_Regularized_Cost_user.ipynb 7KB
C1_W3_Lab02_Decision_Boundary_Soln.ipynb 7KB
C1_W3_Lab02_Decision_Boundary_user.ipynb 7KB
C1_W3_Lab02_Decision_Boundary.ipynb 7KB
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