文件名称:
Hands－On.Machine.Learning.with.Scikit－Learn.and.TensorFlow.2017
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文件大小: 32mb
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上传时间: 2018-06-08
详细说明： This book is organized in two parts. Part I, The Fundamentals of Machine Learning, covers the following topics: What is Machine Learning? What problems does it try to solve? What are the main categories and fundamental concepts of Machine Learning systems? The main steps in a typical Machine Learning project. Learning by fitting a model to data. Optimizing a cost function. Handling, cleaning, and preparing data. Selecting and engineering features. Selecting a model and tuning hyperparameters using cross-validation. The main chal lenges of Machine Learning, in particular underfitting and overfitting (the bias/variance tradeoff). Reducing the dimensionality of the training data to fight the curse of dimensionality. The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods. Part II, Neural Networks and Deep Learning, covers the following topics: What are neural nets? What are they good for? Building and training neural nets using TensorFlow. The most important neural net architectures: feedforward neural nets, convolutional nets, recurrent nets, long short-term memory (LSTM) nets, and autoencoders. Techniques for training deep neural nets. Scaling neural networks for huge datasets. Reinforcement learning. The first part is based mostly on Scikit-Learn while the second part uses TensorFlow. ...展开收缩
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