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文件名称:Getting Started with TensorFlow
文件大小:4.93MB
文件格式:PDF
更新时间:2021-04-12 09:20:40
machine learning tensorflow
This book is organized in two parts. Part I, e 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 challenges of Machine Learning, in particular underfitting and overfit‐ ting (the bias/variance tradeoff).
• Reducing the dimensionality of the training data to fight the curse of dimension‐ ality.
• 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, convolu‐ tional nets, recurrent nets, long short-term memory (LSTM) nets, and autoen‐ coders.
• 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.