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.