tailieunhanh - Machine Learning for Hackers

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. | Case Studies and Algorithms to Get You Started for Hackers O REILLY Drew Conway John Myles white Programming Machine Learning Machine Learning for Hackers If you re an experienced programmer interested in crunching data this book will get you started with machine learning a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles white help you understand machine learning and statistics tools through a scries of hands-on case studies instead of a traditional mathheavy presentation. Each chapter focuses on a specific problem in machine learning such as classification prediction optimization and recommendation. Using the R programming language you ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background including business government and academic research. This book provides excellent case studies of a dozen different techniques in machine learning. With a focus on process rather than cookbooks or theory it should be accessible to anybody with a programming background and a quantitative mind. MaxShron OkCupid Develop a naive Bayesian classifier to determine if an email is spam based only on its text Use linear regression to predict the number of page views for the top 1 000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast US Senators statistically based on their voting records Build a whom to follow recommendation system from Twitter data Drew Conway a PhD candidate in Politics at NYU studies international relations conflict and terrorism using mathematics statistics and computer science tools. He spent years as an analyst in the US intelligence and defense communities. John Myles White is a PhD student in the Princeton Psychology Department where he studies how humans make decisions both theoretically and experimentally. He

• Hệ điều hành
• Machine Learning
• Hackers
• Drew Conway
• R programming language
• John Myles White
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Max margin learning
• Multi instance learning
• Reinforcement learning
• Delayed rewards
• Machine Learning Introduction
• Machine Learning Work
• Types of Machine Learning
• Supervised Learning
• Forms of Supervised Learning
• Bayesian Learning
• Back propagation
• Boltzmann machines
• Linear prediction
• Linear regression models
• Maximum likelihood
• Gaussian distributions
• Nonlinear ridge regression risk
• Cross validation
• Newton’s algorithm
• Gradient descent algorithm
• Logistic regression
• Logistic regression likelihood
• Formulate layers
• Neural networks
• Artificial neural networks
• Convolutional neural networks
• Design convolutional layers
• Recurrent nets
• Recurrent neural networks
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Linear regression
• Learning a regression function
• Unsupervised learning
• Partition based clustering
• The sewing machine classroom
• Learning the ins
• Outs of your machine
• Mastering the materials
• Demystifying your machine
• Increasing your sphere
• Deep Belief Network
• Restricted Boltzmann Machine
• Support Vector Machine
• Automatic CNC program
• Challenges in Machine Learning
• Data Mining
• Learning from non vectorial data
• Beyond classification and regression
• Distributed data mining
• Machine learning bottleneck
• Introduction to machine learning
• Bayesian decision theory
• Parametric methods
• Multivariate methods
• BMC Bioinformatics
• Extreme learning machine
• Correntropy induced loss
• Machine learning method
• K nearest neighbors
• Neighbor based learnin
• Decision tree
• Random forest
• Support vector machines
• Separating hyperplane
• Artificial neural network
• Rectified linear unit
• Normalization methods
• Deep learning
• Representation learning
• Phenotype prediction
• Molecular diagnostics