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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-and-deep-learning-using-tensorflow/
课程评论：没有评论
课程名称：使用TensorFlow进行机器学习和深度学习 课程概述： 本课程适合那些希望深入了解机器学习、神经网络、深度学习、深度神经网络（DNN）和卷积神经网络（CNN）的学习者。课程内容详细阐述，概念以逐步方式发展，让您对每一部分材料都有充分的理解。有时课程讲师甚至会在单一幻灯片上花费超过10分钟的时间，以便您能够跟上课程进度，更好地理解所讲解的内容。课程还提供了实践示例，旨在让您熟悉TensorFlow 2的某些功能，这对于将来需要运行大型复杂神经网络的项目尤为重要。您可以下载这些实践示例。 覆盖的主题包括： - 机器学习概念 - 线性回归及参数计算步骤 - 使用均方误差（MSE）成本函数的线性回归梯度下降 - 逻辑回归及其分类方法 - 非线性决策边界的建模 - 神经网络的引入与复杂决策机制 - 多层神经网络权重的计算与反向传播技术 - 深度神经网络（DNN）图像分类 - 卷积神经网络（CNN）的基础与架构 - 处理过拟合与欠拟合问题的方法 - 使用Google Colab进行模型开发与优化 - 功能性API与迁移学习 课程适合有一定基础的学习者，通过观看前两个视频，您可以对课程的整体结构和内容有更清晰的理解。
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.Hand-on examples are available for you to download.Please watch the first two videos to have a better understanding of the course.TOPICS COVEREDWhat is Machine Learning?Linear RegressionSteps to Calculate the ParametersLinear Regression-Gradient Descent using Mean Squared Error (MSE) Cost FunctionLogistic Regression: ClassificationDecision BoundarySigmoid FunctionNon-Linear Decision BoundaryLogistic Regression: Gradient DescentGradient Descent using Mean Squared Error Cost FunctionProblems with MSE Cost Function for Logistic RegressionIn Search for an Alternative Cost-FunctionEntropy and Cross-EntropyCross-Entropy: Cost Function for Logistic RegressionGradient Descent with Cross Entropy Cost FunctionLogistic Regression: Multiclass ClassificationIntroduction to Neural NetworkLogical OperatorsModeling Logical Operators using Perceptron(s)Logical Operators using Combination of PerceptronNeural Network: More Complex Decision MakingBiological NeuronWhat is Neuron? Why Is It Called the Neural Network?What Is An Image?My "Math" CAT. Anatomy of an ImageNeural Network: Multiclass ClassificationCalculation of Weights of Multilayer Neural Network Using Backpropagation TechniqueHow to Update the Weights of Hidden Layers using Cross Entropy Cost FunctionHands OnGoogle Colab. Setup and Mounting Google Drive (Colab)Deep Neural Network (DNN) Based Image Classification Using Google Colab. & TensorFlow (Colab)Introduction to Convolution Neural Networks (CNN)CNN ArchitectureFeature Extraction, Filters, Pooling LayerHands OnCNN Based Image Classification Using Google Colab & TensorFlow (Colab)Methods to Address Overfitting and Underfitting ProblemsRegularization, Data Augmentation, Dropout, Early StoppingHands OnDiabetes prediction model development (Colab)Fixing problems using Regularization, Dropout, and Early Stopping (Colab)Hands On: Various TopicsSaving Weights and Loading the Saved Weights (Colab)How To Split a Long Run Into Multiple Smaller RunsFunctional API and Transfer Learning (Colab)How to Extract the Output From an Intermediate Layer of an Existing Model (Colab), and add additional layers to it to build a new model.
