“…Koza, former CEO and chairman, Scientific Games Inc. and Consulting Professor, Stanford University ""Dr. Goldberg provides the road map for engineer…”

“…A WALL STREET JOURNAL AND USA TODAY BESTSELLERAn actionable strategy guide for busy professionals who want to level up their management game In The Manager's Handbook, Stanford Graduate School of Business faculty member, serial entrepreneur, and investor in over 100 companies, David Dodson, delivers an insightful work that describes, in highly practical detail, five skills every great manager needs to know if they want to get things done. …”

“…Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. …”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research…”

Tabla de Contenidos: “…准备工作 -- 操作步 -- 6.3 使用Java 检测句子 -- 准备工作 -- 操作步 -- 6.4 使用OpenNLP 检测标 单 与句子 -- 准备工作 -- 操作步 -- 6.5 使用Stanford CoreNLP 从标 中提取 根、 性 以及 别命名实体 -- 准备工作 -- 操作步 -- 6.6 使用 Java 8 借助余弦相似性测度测 文本相似度 -- 准备工作 -- 操作步 -- 6.7 使用Mallet 从文本文档提取主 -- 准备工作 -- 操作步 -- 6.8 使用Mallet 对文本文档 分类 -- 准备工作 -- 操作步 -- 6.9 使用Weka 对文本文档 分类 -- 准备工作 -- 操作步 -- 第7章 处理大数据 -- 7.1 简介 -- 7.2 使用Apache Mahout 练在线 回归模型 -- 准备工作 -- 操作步 -- 7.3 使用Apache Mahout 应用在线 回归模型 -- 准备工作 -- 操作步 -- 7.4 使用Apache Spark 决简单的文本挖掘 -- 准备工作 -- 操作步 -- 7.5 使用MLib 的K 均值算法做 类 -- 准备工作 -- 操作步 -- 7.6 使用MLib 创建线性回归模型 -- 准备工作 -- 操作步 -- 7.7 使用 MLib 的 机森林模型对数据点 分类 -- 准备工作 -- 操作步 -- 第8章 数据深度学习 -- 8.1 简介 -- 8.2 使用DL4j 创建Word2vec 神经网络 -- 操作方法 -- 工作原理 -- 更多内容 -- 8.3 使用 DL4j 创建深度信念神经网络 -- 操作步 -- 工作原理 -- 8.4 使用DL4j 创建深度 动编码器 -- 操作步 -- 工作原理 -- 第9章 数据可 化 -- 9.1 简介 -- 9.2 绘制2D 正弦曲线 -- 准备工作 -- 操作步 -- 9.3 绘制直方图 -- 准备工作 -- 操作步 -- 9.4 绘制条形图 -- 准备工作 -- 操作步 -- 9.5 绘制箱线图或箱 图 -- 准备工作 -- 操作步 -- 9.6 绘制散点图 -- 准备工作 -- 操作步 -- 9.7 绘制甜圈图 -- 准备工作 -- 操作步 -- 9.8 绘制 积图 -- 准备工作 -- 操作步…”

Tabla de Contenidos: “…-- Hiring for Complementary Skills -- Going Deeper: Getting to know Our Motivations -- Focusing on Adding Value, Regardless of title -- Embrace Improv as a Way to become A more Present, Collaborative Leader -- Get To know Yourself Through "personal Retrospectives" -- Exercise: Personal Retrospective, by April Wensel -- Exercise: Happiness and Fulfillment Timeline by Dave King (adapted From stanford Graduate School of business) -- Understanding Our Privilege and How It Impacts Our Work -- Embracing All of Ourselves, Including Our "shadow" Sides -- Are You Outsourcing a Part of Yourself to Your Team? …”

Tabla de Contenidos: “…-- Topic modeling with Spark MLlib and Stanford NLP -- Implementation -- Step 1 - Creating a Spark session -- Step 2 - Creating vocabulary and tokens count to train the LDA after text pre-processing -- Step 3 - Instantiate the LDA model before training -- Step 4 - Set the NLP optimizer -- Step 5 - Training the LDA model -- Step 6 - Prepare the topics of interest -- Step 7 - Topic modelling -- Step 8 - Measuring the likelihood of two documents -- Other topic models versus the scalability of LDA -- Deploying the trained LDA model -- Summary -- Chapter 6: Developing Model-based Movie Recommendation Engines -- Recommendation system -- Collaborative filtering approaches…”

Tabla de Contenidos: “…The curse of dimensionality, the data science plague -- Optimizing KMeans -- Story mutation -- The Equilibrium state -- Tracking stories over time -- Building a streaming application -- Streaming KMeans -- Visualization -- Building story connections -- Summary -- Chapter 11: Anomaly Detection on Sentiment Analysis -- Following the US elections on Twitter -- Acquiring data in stream -- Acquiring data in batch -- The search API -- Rate limit -- Analysing sentiment -- Massaging Twitter data -- Using the Stanford NLP -- Building the Pipeline -- Using Timely as a time series database -- Storing data -- Using Grafana to visualize sentiment -- Number of processed tweets -- Give me my Twitter account back -- Identifying the swing states -- Twitter and the Godwin point -- Learning context -- Visualizing our model -- Word2Graph and Godwin point -- Building a Word2Graph -- Random walks -- A Small Step into sarcasm detection -- Building features -- #LoveTrumpsHates -- Scoring Emojis -- Training a KMeans model -- Detecting anomalies -- Summary -- Chapter 12: TrendCalculus -- Studying trends -- The TrendCalculus algorithm -- Trend windows -- Simple trend -- User Defined Aggregate Functions -- Simple trend calculation -- Reversal rule -- Introducing the FHLS bar structure -- Visualize the data -- FHLS with reversals -- Edge cases -- Zero values -- Completing the gaps -- Stackable processing -- Practical applications -- Algorithm characteristics -- Advantages -- Disadvantages -- Possible use cases -- [Chart annotation] -- Chart annotation -- Co-trending -- Data reduction -- Indexing -- Fractal dimension -- Streaming proxy for piecewise linear regression -- Summary -- Chapter 13: Secure Data -- Data security -- The problem -- The basics -- Authentication and authorization -- Access control lists (ACL) -- Role-based access control (RBAC) -- Access -- Encryption…”

Tabla de Contenidos: “…MCC -- Human evaluation -- Benchmark tasks and datasets -- Defining the SuperGLUE benchmark tasks -- Running downstream tasks -- The Corpus of Linguistic Acceptability (CoLA) -- Stanford Sentiment TreeBank (SST-2) -- Microsoft Research Paraphrase Corpus (MRPC) -- Winograd schemas -- Summary -- Questions -- References -- Further reading -- Chapter 4: Advancements in Translations with Google Trax, Google Translate, and Gemini -- Defining machine translation -- Human transductions and translations -- Machine transductions and translations -- Evaluating machine translations -- Preprocessing a WMT dataset -- Preprocessing the raw data -- Finalizing the preprocessing of the datasets -- Evaluating machine translations with BLEU -- Geometric evaluations -- Applying a smoothing technique -- Translations with Google Trax -- Installing Trax -- Creating the Original Transformer model -- Initializing the model using pretrained weights -- Tokenizing a sentence -- Decoding from the Transformer -- De-tokenizing and displaying the translation -- Translation with Google Translate -- Translation with a Google Translate AJAX API Wrapper -- Implementing googletrans -- Translation with Gemini -- Gemini's potential -- Summary -- Questions -- References -- Further reading -- Chapter 5: Diving into Fine-Tuning through BERT -- The architecture of BERT -- The encoder stack -- Preparing the pretraining input environment -- Pretraining and fine-tuning a BERT model -- Fine-tuning BERT -- Defining a goal -- Hardware constraints -- Installing Hugging Face Transformers -- Importing the modules -- Specifying CUDA as the device for torch -- Loading the CoLA dataset -- Creating sentences, label lists, and adding BERT tokens -- Activating the BERT tokenizer -- Processing the data -- Creating attention masks -- Splitting the data into training and validation sets…”

“…PSB 2021 will be held on a virtual platform at http://psb.stanford.edu/ on January 5–7, 2021. Tutorials and workshops will be offered prior to the start of the conference. …”

Tabla de Contenidos: “…-- 7.2 Model and performance measures -- 7.3 Simplest timer schemes -- 7.4 Timing wheels -- 7.5 Hashed wheels -- 7.6 Hierarchical wheels -- 7.7 BSD implementation -- 7.8 Google Carousel implementation -- 7.9 Obtaining finer granularity timers -- 7.10 Conclusions -- 7.11 Exercises -- 8 Demultiplexing -- 8.1 Opportunities and challenges of early demultiplexing -- 8.2 Goals -- 8.3 CMU/Stanford packet filter: pioneering packet filters -- 8.4 Berkeley packet filter: enabling high-performance monitoring -- 8.5 Pathfinder: factoring out common checks -- 8.6 Dynamic packet filter: compilers to the rescue -- 8.7 Conclusions -- 8.8 Exercises -- 9 Protocol processing -- 9.1 Buffer management -- 9.1.1 Buffer allocation -- Segregated pool allocator -- Linux allocator -- Batch allocator -- 9.1.2 Sharing buffers -- Buffer stealing -- Dynamic thresholds -- 9.2 Cyclic redundancy checks and checksums -- 9.2.1 Cyclic redundancy checks -- Naive implementation -- Implementation using linear feedback shift registers -- Faster implementations -- 9.2.2 Internet checksums -- Header checksum -- 9.2.3 Finessing checksums -- 9.3 Generic protocol processing -- 9.3.1 UDP processing -- 9.4 Reassembly -- 9.4.1 Efficient reassembly -- 9.5 Conclusions -- 9.6 Exercises…”

Tabla de Contenidos: “…NLTK: Text Mining in Python -- A.3.5. Stanford Parser and Part of Speech Tagger -- A.3.6. …”

Tabla de Contenidos: “…ANÁLISIS DE SENTIMIENTOS -- 6.3.2.1 OPINIONFINDER (OFV2) -- 6.3.2.2 STANFORD CORENLP (RNTN) -- 6.3.2.3 SENTUAH -- 6.3.3. …”

Tabla de Contenidos: “….) -- 3.6.1 Robot cilíndrico -- 3.6.2 Robot antropomórfico -- 3.6.3 Eslabón esférico -- 3.6.4 Robot TQ MA2000 -- 3.6.5 Robot manipulador Stanford -- 3.6.6 Robot manipulador SCARA -- 3.6.7 Robot humanoide Dany Walker -- 3.7 RESUMEN DEL CAPÍTULO -- 3.8 EJERCICIOS RECOMENDADOS -- 3.9 LECTURAS RECOMENDADAS -- 4 CINEMÁTICA INVERSA -- 4.1 EL PROBLEMA DE LA CINEMÁTICA INVERSA -- 4.1.1 Solución al problema de cinemática inversa -- 4.1.2 Método geométrico -- 4.1.3 Distancias de ajuste -- 4.1.4 Desacoplamiento cinemático -- 4.1.5 Solución para el robot antropomórfico de 6-DOF -- 4.1.6 Solución del robot SCARA -- 4.1.7 Solución del robot SCARA en Matlab© -- 4.2 MÉTODOS ANALÍTICOS E ITERATIVOS -- 4.3 SOLUCIÓN ITERATIVA DEL PROBLEMA DE CINEMÁTICA INVERSA -- 4.3.1 Convergencia de la solución iterativa -- 4.3.2 Cálculo de la cinemática inversa en Matlab© -- 4.4 RESUMEN DEL CAPÍTULO -- 4.5 EJERCICIOS PROPUESTOS -- 4.6 BIBLIOGRAFÍA RECOMENDADA -- 5 CINEMÁTICA DIFERENCIAL -- 5.1 VELOCIDAD LINEAL Y ROTACIONAL -- 5.1.1 Velocidad lineal -- 5.1.2 Velocidad rotacional -- 5.2 EL VECTOR DE VELOCIDAD -- 5.2.1 Simulación del vector de velocidad en Matlab©…”

“…He spent many years as a professor of Computer Science at Stanford University, where he led the Stanford Visualization Group. …”

“…Analytical chapters and recommendations -Roland Berger, Founder, Roland Berger Foundation -David B. Grusky, Director, Stanford Center for the Study of Poverty & Inequality -Christopher Wimer, Associate Director, Collaboration for Poverty Research, Stanford Center for the Study of Poverty & Inequality…”

“…She’s on the board at Opera Software; writes on mobile for the Huffington Post; and lectures at Stanford. She has an MBA from Stanford and an MS in electrical engineering…”

“…Søren Asmussen is a professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is the Thomas Ford professor of Engineering at Stanford University…”