Tabla de Contenidos: “…Opening existing notebooks -- Saving notebooks -- Performing Common Tasks -- Creating code cells -- Creating text cells -- Creating special cells -- Editing cells -- Moving cells -- Using Hardware Acceleration -- Executing the Code -- Getting Help -- Chapter 4 Performing Essential Data Manipulations Using Python -- Performing Calculations Using Vectors and Matrixes -- Understanding scalar and vector operations -- Performing vector multiplication -- Creating a matrix is the right way to start -- Multiplying matrixes -- Defining advanced matrix operations -- Creating Combinations the Right Way -- Distinguishing permutations -- Shuffling combinations -- Facing repetitions -- Getting the Desired Results Using Recursion -- Explaining recursion -- Eliminating tail call recursion -- Performing Tasks More Quickly -- Considering divide and conquer -- Distinguishing between different possible solutions -- Chapter 5 Developing a Matrix Computation Class -- Avoiding the Use of NumPy -- Understanding Why Using a Class is Important -- Building the Basic Class -- Creating a matrix -- Printing the resulting matrix -- Accessing specific matrix elements -- Performing scalar and matrix addition -- Performing multiplication -- Manipulating the Matrix -- Transposing a matrix -- Calculating the determinant -- Flattening the matrix -- Part 2 Understanding the Need to Sort and Search -- Chapter 6 Structuring Data -- Determining the Need for Structure -- Making it easier to see the content -- Matching data from various sources -- Considering the need for remediation -- Stacking and Piling Data in Order -- Ordering in stacks -- Using queues -- Finding data using dictionaries -- Working with Trees -- Understanding the basics of trees -- Building a tree -- Representing Relations in a Graph -- Going beyond trees -- Building graphs -- Chapter 7 Arranging and Searching Data…”

Tabla de Contenidos: “…-- 4.4 Dealing with incorrect values: Vehicles -- 4.5 Dealing with inconsistent values: Location -- 4.6 Going the distance: Locations -- 4.7 Fixing type mismatches -- 4.8 Dealing with rows that still contain bad data -- 4.9 Creating derived columns -- 4.10 Preparing non-numeric data to train a deep learning model -- 4.11 Overview of the end-to-end solution -- Summary -- 5 Preparing and building the model -- 5.1 Data leakage and features that are fair game for training the model -- 5.2 Domain expertise and minimal scoring tests to prevent data leakage -- 5.3 Preventing data leakage in the streetcar delay prediction problem -- 5.4 Code for exploring Keras and building the model -- 5.5 Deriving the dataframe to use to train the model -- 5.6 Transforming the dataframe into the format expected by the Keras model -- 5.7 A brief history of Keras and TensorFlow -- 5.8 Migrating from TensorFlow 1.x to TensorFlow 2 -- 5.9 TensorFlow vs. PyTorch -- 5.10 The structure of a deep learning model in Keras -- 5.11 How the data structure defines the Keras model -- 5.12 The power of embeddings -- 5.13 Code to build a Keras model automatically based on the data structure -- 5.14 Exploring your model -- 5.15 Model parameters -- Summary -- 6 Training the model and running experiments -- 6.1 Code for training the deep learning model -- 6.2 Reviewing the process of training a deep learning model…”

Texto completo en Odilo

Tabla de Contenidos: “…3.8.1 Further reading for least confidence sampling -- 3.8.2 Further reading for margin of confidence sampling -- 3.8.3 Further reading for ratio of confidence sampling -- 3.8.4 Further reading for entropy-based sampling -- 3.8.5 Further reading for other machine learning models -- 3.8.6 Further reading for ensemble-based uncertainty sampling -- Summary -- 4 Diversity sampling -- 4.1 Knowing what you don't know: Identifying gaps in your model's knowledge -- 4.1.1 Example data for diversity sampling -- 4.1.2 Interpreting neural models for diversity sampling -- 4.1.3 Getting information from hidden layers in PyTorch -- 4.2 Model-based outlier sampling -- 4.2.1 Use validation data to rank activations -- 4.2.2 Which layers should I use to calculate model-based outliers? …”

Tabla de Contenidos: “…Endpoint Security Groups -- ACI Contracts -- Consumer and Provider EPGs -- Contract Configuration -- Contract Scope -- Contract Subject -- Contract Filter -- Contract Application to EPGs/ESGs -- Contract Zoning Rules on Leaf Switches -- Endpoint Classification and Zoning Enforcement -- EPG/ESG Preferred Groups -- VRF vzAny Object -- Intra-EPG Isolation and Contracts -- Zone Rules Verification and Troubleshooting -- show system internal policy-mgr stats -- show logging ip access-list internal packet-log deny -- APIC contract_parser.py -- Contract Policy TCAM Utilization -- Naming Convention for ACI Application Policies -- Summary -- Chapter 6: Fabric Forwarding (and Troubleshooting) -- ACI Data Plane - iVXLAN Encapsulation -- 1) Outer MAC Header -- 2) Outer IP Header -- 3) UDP Header -- 4) VXLAN Header -- 5) Original Layer-2 Frame -- Fabric Control Plane Mechanisms Reviewed -- ACI Forwarding Scenarios -- Layer 2 Forwarding -- Multi-Destination (ARP) Forwarding in a Layer 2 Bridge Domain -- Known Layer 2 Unicast -- Unknown Layer 2 Unicast -- Bridge Domain in Flood Mode -- Bridge Domain in Hardware Proxy Mode -- Layer 2 Forwarding Summary -- Layer 3 Forwarding -- ARP Processing in a Layer 3 Bridge Domain -- Unknown Layer 3 Unicast -- Known Layer 3 Unicast -- External Forwarding in a Layer 3 Bridge Domain -- Layer 3 Forwarding Summary -- Multi-Pod Forwarding -- Multi-Pod Control Plane -- Multi-Pod Data Plane -- Multi-Destination Traffic Delivery -- Multi-Site Forwarding -- Name-Space Normalization (Translation) -- Additional Troubleshooting Toolset for Fabric Forwarding -- Endpoint Tracker -- Embedded Logic Analyzer Module -- fTriage -- Switch Port Analyzer -- SPAN Configuration -- Visibility &amp -- Troubleshooting Tool -- Interface Drops Analysis -- Summary -- Chapter 7: External Layer 2 and Layer 3 Connectivity -- Layer 2 External Connectivity…”

Tabla de Contenidos: “….) -- 7.2.1 Identificación -- 7.2.2 Creación -- 7.2.3 Aceptación -- 7.2.4 Aplicación -- 7.2.5 Acreditación -- 7.3 MÉTODO PARA LA MEJORA DE PROCESOS SOFTWARE -- 7.3.1 Mejora de procesos en PyMEs -- 7.3.2 Marco metodológico de Competisoft -- 7.3.3 Investigación-acción en Competisoft -- 7.3.4 Estudio de casos en Competisoft -- 7.4 LECTURAS RECOMENDADAS -- 7.5 SITIOS WEB RECOMENDADOS -- 7.6 HERRAMIENTAS RECOMENDADAS -- ACRÓNIMOS -- BIBLIOGRAFÍA…”

Tabla de Contenidos: “…1.15.5 Superconductors -- 1.15.6 3D printing -- 1.15.7 Autonomous vehicle -- 1.16 Conclusion -- References -- Chapter 2: Advances of deep learning and related applications -- 2.1 Introduction -- 2.2 Deep learning techniques -- 2.3 Multilayer perceptron -- 2.4 Convolutional neural network -- 2.5 Recurrent neural network -- 2.6 Long-term short-term memory -- 2.7 GRU -- 2.8 Autoencoders -- 2.9 Attention mechanism -- 2.10 Deep generative models -- 2.11 Restricted Boltzmann machine -- 2.12 Deep belief network -- 2.13 Modern deep learning platforms -- 2.13.1 PyTorch -- 2.13.2 TensorFlow -- 2.13.3 Keras -- 2.13.4 Caffe (Convolutional architecture for fast feature embedding) and Caffe2 -- 2.13.5 Deeplearning4j -- 2.13.6 Theano -- 2.13.7 Microsoft cognitive toolkit (CNTK) -- 2.14 Challenges of deep learning -- 2.15 Applications of deep learning -- 2.16 Conclusion -- References -- Chapter 3: Machine learning for big data and neural networks -- 3.1 Introduction -- 3.2 Machine learning and fundamentals -- 3.2.1 Supervised learning -- 3.2.2 Decision trees -- 3.2.3 Linear regression -- 3.2.4 Naive Bayes -- 3.2.5 Logistic regression -- 3.3 Unsupervised learning -- 3.3.1 K-Means algorithm -- 3.3.2 Principal component analysis -- 3.4 Reinforcement learning -- 3.5 Machine learning in large-scale data -- 3.6 Data analysis in big data -- 3.7 Predictive modelling -- 3.7.1 Understanding customer behavior and preferences -- 3.7.2 The role of supply chain and performance management in organizational success -- 3.7.3 Management of quality and enhancement -- 3.7.4 Risk mitigation and detection of fraud -- 3.8 Neural networks -- 3.8.1 Artificial neural network -- 3.8.2 RNN -- 3.8.3 CNN -- 3.8.4 Deep learning using convolutional neural networks to find building defects -- 3.9 Data generation and manipulation -- 3.9.1 Generative Adversarial Networks…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Acknowledgments -- General Introduction -- Chapter 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning -- 1.1 Learning Styles for Machine Learning -- 1.1.1 Supervised Learning -- 1.1.1.1 Overfitting and Underfitting -- 1.1.1.2 K-Folds Cross-Validation -- 1.1.1.3 Train/Test Split -- 1.1.1.4 Confusion Matrix -- 1.1.1.5 Loss Functions -- 1.1.2 Unsupervised Learning -- 1.1.3 Semi-Supervised Learning -- 1.1.4 Reinforcement Learning -- 1.2 Essential Python Tools for Machine Learning -- 1.2.1 Data Manipulation with Python -- 1.2.2 Python Machine Learning Libraries -- 1.2.2.1 Scikit-learn -- 1.2.2.2 TensorFlow -- 1.2.2.3 Keras -- 1.2.2.4 PyTorch -- 1.2.3 Jupyter Notebook and JupyterLab -- 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs -- 1.3.1 Installation -- 1.3.2 HephAIstos Function -- 1.4 Where to Find the Datasets and Code Examples -- Further Reading -- Chapter 2 Feature Engineering Techniques in Machine Learning -- 2.1 Feature Rescaling: Structured Continuous Numeric Data -- 2.1.1 Data Transformation -- 2.1.1.1 StandardScaler -- 2.1.1.2 MinMaxScaler -- 2.1.1.3 MaxAbsScaler -- 2.1.1.4 RobustScaler -- 2.1.1.5 Normalizer: Unit Vector Normalization -- 2.1.1.6 Other Options -- 2.1.1.7 Transformation to Improve Normal Distribution -- 2.1.1.8 Quantile Transformation -- 2.1.2 Example: Rescaling Applied to an SVM Model -- 2.2 Strategies to Work with Categorical (Discrete) Data -- 2.2.1 Ordinal Encoding -- 2.2.2 One-Hot Encoding -- 2.2.3 Label Encoding -- 2.2.4 Helmert Encoding -- 2.2.5 Binary Encoding -- 2.2.6 Frequency Encoding -- 2.2.7 Mean Encoding -- 2.2.8 Sum Encoding -- 2.2.9 Weight of Evidence Encoding -- 2.2.10 Probability Ratio Encoding -- 2.2.11 Hashing Encoding -- 2.2.12 Backward Difference Encoding…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Authors -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting started -- Obtaining and installing Python 3 -- Windows -- macOS -- Linux -- Starting Python command line REPL -- Leaving the REPL -- Windows -- Unix -- Code structure and significant indentation -- Python culture -- Importing standard library modules -- Getting help() -- Counting fruit with math.factorial() -- Different types of numbers -- Scalar data types: integers, floats, None and bool -- int -- float -- Special floating point values -- Promotion to float -- None -- bool -- Relational operators -- Rich comparison operators -- Control flow: if-statements and while-loops -- Conditional control flow: The if-statement -- if...else -- if...elif...else -- Conditional repetition: the while-loop -- Exiting loops with break -- Summary -- Chapter 2: Strings and Collections -- str - an immutable sequence of Unicode code points -- String quoting styles -- Moment of zen -- Concatenation of adjacent strings -- Multiline strings and newlines -- Raw strings -- The str constructor -- Strings as sequences -- String methods -- Strings with Unicode -- The bytes type - an immutable sequence of bytes -- Literal bytes -- Converting between bytes and str -- list - a sequence of objects -- The dict type - associating keys with values -- The For-loops - iterating over series of items -- Putting it all together -- Summary -- Chapter 3: Modularity -- Organizing code in a .py file -- Running Python programs from the operating system shell -- Importing modules into the REPL -- Defining functions -- Organizing our module into functions -- The __name__ type and executing modules from the command line -- The Python execution model -- The difference between modules, scripts, and programs…”

Tabla de Contenidos: “…Deploying Mezzanine with Ansible -- Listing Tasks in a Playbook -- Organization of Deployed Files -- Variables and Secret Variables -- Using Iteration (with_items) to Install Multiple Packages -- Adding the Sudo Clause to a Task -- Updating the Apt Cache -- Checking Out the Project Using Git -- Installing Mezzanine and Other Packages into a virtualenv -- Complex Arguments in Tasks: A Brief Digression -- Creating the Database and Database User -- Generating the local_settings.py File from a Template -- Running django-manage Commands -- Running Custom Python Scripts in the Context of the Application -- Setting Service Configuration Files -- Enabling the Nginx Configuration -- Installing TLS Certificates -- Installing Twitter Cron Job -- The Full Playbook -- Running the Playbook Against a Vagrant Machine…”

Tabla de Contenidos: “…Web Scraping -- Project: mapit.py with the webbrowser Module -- Step 1: Figure Out the URL -- Step 2: Handle the Command Line Arguments -- Step 3: Handle the Clipboard Content and Launch the Browser -- Ideas for Similar Programs -- Downloading Files from the Web with the requests Module -- Downloading a Web Page with the requests.get() Function -- Checking for Errors -- Saving Downloaded Files to the Hard Drive -- HTML -- Resources for Learning HTML -- A Quick Refresher…”

Tabla de Contenidos: “…Real-life applications for labeling audio data -- Audio data fundamentals -- Hands-on with analyzing audio data -- Example code for loading and analyzing sample audio file -- Best practices for audio format conversion -- Example code for audio data cleaning -- Extracting properties from audio data -- Tempo -- Chroma features -- Mel-frequency cepstral coefficients (MFCCs) -- Zero-crossing rate -- Spectral contrast -- Considerations for extracting properties -- Visualizing audio data with matplotlib and Librosa -- Waveform visualization -- Loudness visualization -- Spectrogram visualization -- Mel spectrogram visualization -- Considerations for visualizations -- Ethical implications of audio data -- Recent advances in audio data analysis -- Troubleshooting common issues during data analysis -- Troubleshooting common installation issues for audio libraries -- Summary -- Chapter 11: Labeling Audio Data -- Technical requirements -- Downloading FFmpeg -- Azure Machine Learning -- Real-time voice classification with Random Forest -- Transcribing audio using the OpenAI Whisper model -- Step 1 - importing the Whisper model -- Step 2 - loading the base Whisper model -- Step 3 - setting up FFmpeg -- Step 4 - transcribing the YouTube audio using the Whisper model -- Classifying a transcription using Hugging Face transformers -- Hands-on - labeling audio data using a CNN -- Exploring audio data augmentation -- Introducing Azure Cognitive Services - the speech service -- Creating an Azure Speech service -- Speech to text -- Speech translation -- Summary -- Chapter 12: Hands-On Exploring Data Labeling Tools -- Technical requirements -- Azure Machine Learning data labeling -- Label Studio -- pyOpenAnnotate -- Data labeling using Azure Machine Learning -- Benefits of data labeling with Azure Machine Learning -- Data labeling steps using Azure Machine Learning…”

Tabla de Contenidos: “…-- Using Nautilus as a Network Browser -- Reading Data CDs and DVDs -- Burning Data CDs and DVDs -- USB Storage Devices -- Working with Bluetooth Devices -- Backing Up Your Files -- Recovering from a Backup -- Removing Unwanted Files -- Project 7: Creating and Extracting Compressed Files -- 8: Dressing up the Bird - Customizing the Look and Feel of Your System -- Project 8A: Creating a New User Account -- Project 8B: Customizing Your Desktop Environment -- Font Feathered Frenzy: Changing Your Fonts -- Project 8C: Installing TrueType Fonts -- Project 8D: Changing Your Login Screen -- Choosing a Screensaver -- Taking Screenshots -- Customizing Visual Effects -- 9: Simple Kitten Ways - Getting to Know the Linux Terminal and Command Line . . . and the Cool Things It Can Do -- Meet the Terminal -- Some Goofy Yet Useful Fun with the Command Terminal -- Nontoxic Commands -- Commands with Some Teeth -- A Couple of Other Biters You'll Be Using Soon -- Project 9A: Creating a Plan -- Project 9B: More Command Practice with pyWings -- Project 9C: Command Practice Review with Briscola -- Project 9D: Compiling and Installing Programs from Source: Xmahjongg -- Customizing the Terminal -- Tabbed Shell Sessions in the Terminal -- 10: Gutenbird - Setting Up and Using Your Printer and Scanner -- Printers -- Scanners -- 11: Polyglot Penguins - Linux Speaks Your Language -- Typing Nonstandard Characters -- Chinese, Japanese, and Korean Input -- Read-Only Language Support…”

Tabla de Contenidos: “…Intercepting traffic with MiTM attacks -- Summary -- Further reading -- Chapter 11: Delving into Command and Control Tactics -- Technical requirements -- Understanding C2 -- Setting up C2 operations -- Part 1 - Empire client-server model -- Part 2 - Managing users on Empire -- Post-exploitation using Empire -- Part 1 - Creating a listener -- Part 2 - Creating a stager -- Part 3 - Working with agents -- Part 4 - Creating a new agent -- Part 5 - Threat emulation -- Part 6 - Setting up persistence -- Working with Starkiller -- Part 1 - Starkiller -- Part 2 - User management -- Part 3 - Working with modules -- Part 4 - Creating listeners -- Part 5 - Creating stagers -- Part 6 - Interacting with agents -- Part 7 - Credentials and reporting -- Summary -- Further reading -- Chapter 12: Working with Active Directory Attacks -- Technical requirements -- Understanding Active Directory -- Enumerating Active Directory -- Working with PowerView -- Exploring BloodHound -- Part 1 - setting up BloodHound -- Part 2 - remote data collection with BloodHound.py -- Part 3 - data analysis using BloodHound -- Leveraging network-based trust -- Exploiting LLMNR and NetBIOS-NS -- Exploiting SMB and NTLMv2 within Active Directory -- Retrieving the SAM database -- Obtaining a reverse shell -- Summary -- Further reading -- Chapter 13: Advanced Active Directory Attacks -- Technical requirements -- Understanding Kerberos -- Abusing trust on IPv6 with Active Directory -- Part 1: setting up for an attack -- Part 2: launching the attack -- Part 3: taking over the domain -- Attacking Active Directory -- Lateral movement with CrackMapExec -- Vertical movement with Kerberos -- Lateral movement with Mimikatz -- Part 1: setting up the attack -- Part 2: grabbing credentials -- Domain dominance and persistence -- Golden ticket -- Silver ticket -- Skeleton key -- Summary -- Further reading…”

Tabla de Contenidos: “…Looping Over a List with "with_items" -- Handlers -- Using Handlers -- Using Handlers with Multiple Tasks -- Using Handlers with Multiple Tasks and Different Handlers -- Conditional Execution -- Using "ignore_errors" to Continue Execution Even After Failures -- Using "changed_when" to Control When a Task Is Considered Changed -- Using "failed_when" to Control When a Task Is Considered Failed -- Failing a Playbook with "fail" -- Vault -- Editing an Encrypted File -- Updating Encryption Password -- Viewing an Encrypted File -- Encrypting a File -- Encrypting a File with a Password File -- Decrypting a File -- Encrypting a String -- Encrypting a String with a Password File -- Asynchronous Tasks -- Running a Task Asynchronously -- Checking the Status of Asynchronous Tasks -- Roles -- Creating a Role -- Using a Role -- Using a Role with Variables -- Using a Role with Multiple Variables -- Using a Role with Tags -- Ansible Galaxy -- Searching for Roles -- Installing a Role -- Installing a Role with a Specific Version -- Ansible Collections -- Installing a Collection -- Installing a Collection with a Specific Version -- Installing a Collection from a File -- Installing a Collection from a Directory -- Resources About Ansible -- Official Ansible Documentation -- Ansible Blogs and Articles -- Ansible Community and Forums -- Ansible GitHub Repository -- Ansible Videos -- Ansible Tools -- IDE Extensions -- VSCode -- PyCharm -- Sublime -- Vim -- Newsletters -- Afterword -- Your Feedback Matters…”

Tabla de Contenidos: “…封 -- 版权声明 -- 内容提 -- 序 -- 前 -- 源与支持 -- 目录 -- 模块 1 NLTK 基础知 -- 第1章 然 处理简介 -- 1.1 为什么 学习NLP -- 1.2 从Python 的基本知 开始 -- 1.2.1 列 -- 1.2.2 助 -- 1.2.3 正则 式 -- 1.2.4 典 -- 1.2.5 编写函数 -- 1.3 NLTK -- 1.4 一 -- 1.5 本章小结 -- 第2章 文本的整理和清洗 -- 2.1 文本整理 -- 2.2 文本清洗 -- 2.3 句子拆分器 -- 2.4 标 析 -- 2.5 干提取 -- 2.6 形 原 -- 2.7 停用 删 -- 2.8 生僻字删 -- 2.9 拼写校正 -- 2.10 一 -- 2.11 本章小结 -- 第3章 性标注 -- 3.1 什么是 性标注 -- 3.1.1 斯坦福标注器 -- 3.1.2 深入了 标注器 -- 3.1.3 序列标注器 -- 1 N元标注器 -- 2 正则 式标注器 -- 3.1.4 布 尔标注器 -- 3.1.5 基于标注器的机器学习 -- 3.2 命名实体 别 -- NER标注器 -- 3.3 一 -- 3.4 本章小结 -- 第4章 对文本的结构 法分析 -- 4.1 浅层 法分析与深层 法分析 -- 4.2 法分析的两种方法 -- 4.3 为什么 法分析 -- 4.4 不同类型的 法分析器 -- 4.4.1 归下 的 法分析器 -- 4.4.2 移位归约 法分析器 -- 4.4.3 图 法分析器 -- 4.4.4 正则 式 法分析器 -- 4.5 依存分析 -- 4.6 组块化 -- 4.7 信息抽取 -- 4.7.1 命名实体 别 -- 4.7.2 关系抽取 -- 4.8 本章小结 -- 第5章 NLP应用 -- 5.1 构建第一个NLP 应用 -- 5.2 其他的NLP 应用 -- 5.2.1 机器翻 -- 5.2.2 统 机器翻 -- 5.2.3 信息检索 -- 5.2.4 别 -- 5.2.5 文本分类 -- 5.2.6 信息提取 -- 5.2.7 答系统 -- 5.2.8 对 系统 -- 5.2.9 义消歧 -- 5.2.10 主 建模 -- 5.2.11 检测 -- 5.2.12 光学字符 别 -- 5.3 本章小结 -- 第6章 文本分类 -- 6.1 机器学习 -- 6.2 文本分类 -- 6.3 样 -- 6.3.1 朴素 叶斯 -- 6.3.2 决策树 -- 6.3.3 机梯度下 -- 6.3.4 回归 -- 6.3.5 支持向 机 -- 6.4 机森林算法 -- 6.5 文本 类 -- K均值算法 -- 6.6 文本的主 建模 -- 安 gensim -- 6.7 参 料 -- 6.8 本章小结 -- 第7章 网络爬取 -- 7.1 网络爬 -- 7.2 编写第一个爬 程序 -- 7.3 Scrapy 中的数据流 -- 7.3.1 Scrapy 命令 界 -- 7.3.2 -- 7.4 站点地图 -- 7.5 管 -- 7.6 外 参 -- 7.7 本章小结 -- 第8章 与其他Python库一同使用NLTK -- 8.1 NumPy -- 8.1.1 ndarray -- 8.1.2 基本操作 -- 8.1.3 从数组中提取数据 -- 8.1.4 复杂的矩 算 -- 8.2 SciPy -- 8.2.1 线性代数 -- 8.2.2 特征值和特征向 -- 8.2.3 稀疏矩 -- 8.2.4 优化 -- 8.3 Pandas -- 8.3.1 取数据 -- 8.3.2 时序数据 -- 8.3.3 列 换 -- 8.3.4 噪声数据 -- 8.4 Matplotlib -- 8.4.1 subplot -- 8.4.2 添加 -- 8.4.3 散点图 -- 8.4.4 柱状图 -- 8.4.5 3D图 -- 8.5 外 参 -- 8.6 本章小结 -- 第9章 使用Python 社交媒体挖掘 -- 9.1 数据收 -- 推特…”

Tabla de Contenidos: “…Customizing the appearance of your app -- Customizing the launcher icon -- Using custom CSS -- Using custom HTML -- Custom HTML in a simple dashboard -- Using server-side include in a complex dashboard -- Object permissions -- How permissions affect navigation -- How permissions affect other objects -- Correcting permission problems -- App directory structure -- Adding your app to Splunkbase -- Preparing your app -- Confirming sharing settings -- Cleaning up our directories -- Packaging your app -- Uploading your app -- Self-service app management -- Summary -- Chapter 9: Building Advanced Dashboards -- Reasons for working with advanced XML -- Reasons for not working with advanced XML -- Development process -- Advanced XML structure -- Converting simple XML to advanced XML -- Module logic flow -- Understanding layoutPanel -- Panel placement -- Reusing a query -- Using intentions -- stringreplace -- addterm -- Creating a custom drilldown -- Building a drilldown to a custom query -- Building a drilldown to another panel -- Building a drilldown to multiple panels using HiddenPostProcess -- Third-party add-ons -- Google Maps -- Sideview Utils -- The Sideview search module -- Linking views with Sideview -- Sideview URLLoader -- Sideview forms -- Summary -- Chapter 10: Summary Indexes and CSV Files -- Understanding summary indexes -- Creating a summary index -- When to use a summary index -- When to not use a summary index -- Populating summary indexes with saved searches -- Using summary index events in a query -- Using sistats, sitop, and sitimechart -- How latency affects summary queries -- How and when to backfill summary data -- Using fill_summary_index.py to backfill -- Using collect to produce custom summary indexes -- Reducing summary index size -- Using eval and rex to define grouping fields -- Using a lookup with wildcards…”

Tabla de Contenidos: “…Create Customer Data -- Create Order Data -- Create Order Item Data -- Close the Connection -- Perform Queries -- Summary -- Chapter 19 PostgreSQL in C# -- Create the Program -- Install Npgsql -- Connect to the Database -- Delete Old Data -- Create Customer Data -- Create Order Data -- Create Order Item Data -- Display Orders -- Summary -- Exercises -- Chapter 20 Neo4j AuraDB in Python -- Install Neo4j AuraDB -- Nodes and Relationships -- Cypher -- Create the Program -- Install the Neo4j Database Adapter -- Action Methods -- delete_all_nodes -- make_node -- make_link -- execute_node_query -- find_path -- Org Chart Methods -- build_org_chart -- query_org_chart -- Main Program -- Summary -- Chapter 21 Neo4j AuraDB in C# -- Create the Program -- Install the Neo4j Driver -- Action Methods -- DeleteAllNodes -- MakeNode -- MakeLink -- ExecuteNodeQuery -- FindPath -- Org Chart Methods -- BuildOrgChart -- QueryOrgChart -- Main -- Summary -- Chapter 22 MongoDB Atlas in Python -- Not Normal but Not Abnormal -- XML, JSON, and BSON -- Install MongoDB Atlas -- Find the Connection Code -- Create the Program -- Install the PyMongo Database Adapter -- Helper Methods -- person_string -- connect_to_db -- delete_old_data -- create_data -- query_data -- Main Program -- Summary -- Chapter 23 MongoDB Atlas in C# -- Create the Program -- Install the MongoDB Database Adapter -- Helper Methods -- PersonString -- DeleteOldData -- CreateData -- QueryData -- Main Program -- Summary -- Chapter 24 Apache Ignite in Python -- Install Apache Ignite -- Start a Node -- Without Persistence -- With Persistence -- Create the Program -- Install the pyignite Database Adapter -- Define the Building Class -- Save Data -- Read Data -- Demonstrate Volatile Data -- Demonstrate Persistent Data -- Summary -- Chapter 25 Apache Ignite in C# -- Create the Program…”

Tabla de Contenidos: “…-- Discriminative modeling -- Generative modeling -- Bayes' theorem -- Expectation-Maximization (EM) -- Understanding the idea behind LDA -- Dirichlet distribution of topics -- Understanding the structure of LDA -- Variational inference -- Variational E-M -- Gibbs sampling in LDA -- Variational E-M versus Gibbs sampling -- Summary -- Questions -- References -- Chapter 11: LDA Modeling -- Technical requirements -- Text preprocessing -- Preprocessing -- Experimenting with LDA modeling -- A model built on BoW data -- A model built on TF-IDF data -- Building LDA models with a different number of topics -- Models built on BoW data -- Models built on TF-IDF data -- Determining the optimal number of topics -- Using the model to score new documents -- Text preprocessing -- Scoring new texts -- Outcome -- Summary -- Questions -- References -- Chapter 12: LDA Visualization -- Technical requirements -- Designing an infographic -- Data visualization with pyLDAvis -- The interactive graph -- Summary -- Questions -- References -- Chapter 13: The Ensemble LDA for Model Stability -- Technical requirements -- From LDA to Ensemble LDA -- The process of Ensemble LDA -- Understanding DBSCAN and CBDBSCAN -- DBSCAN -- CBDBSCAN (Checkback DBSCAN) -- Building an Ensemble LDA model with Gensim -- Preprocessing the training data -- Creating text representation with BOW and TF-IDF -- Saving the dictionary -- Building the Ensemble LDA model -- Scoring new documents -- Summary -- Questions -- References -- Part 5: Comparison and Applications -- Chapter 14: LDA and BERTopic -- Technical requirements -- Understanding the Transformer model…”

Tabla de Contenidos: “…-- 3.8.4 Listenoperationen -- 3.8.5 Projekt: Zufallsnamen -- 3.8.6 Projekt: Telefonliste -- 3.8.7 Listen durch Comprehensions erzeugen -- 3.9 Zahlen in einer Folge - range()-Funktion -- 3.10 Projekt: Klopfzeichen -- 3.11 Mengen -- 3.11.1 Projekt: Häufigkeit von Buchstaben in einem Text -- 3.12 Projekt: Zufallssounds -- 3.12.1 Wie kommen Töne aus dem Raspberry Pi? -- 3.12.2 Sounds mit PyGame -- 3.12.3 Programmierung -- 3.13 Dictionaries -- 3.13.1 Operationen für Dictionaries -- 3.13.2 Projekt: Morsen -- 3.14 Projekt: Der kürzeste Weg zum Ziel -- 3.15 Aufgaben -- 3.16 Lösungen -- Kapitel 4: Funktionen -- 4.1 Aufruf von Funktionen -- 4.1.1 Unterschiedliche Anzahl von Argumenten -- 4.1.2 Positionsargumente und Schlüsselwort-Argumente -- 4.1.3 Für Experten: Funktionen als Argumente -- 4.2 Definition von Funktionen -- 4.3 Funktionen in der IDLE-Shell testen -- 4.4 Docstrings -- 4.5 Veränderliche und unveränderliche Objekte als Parameter…”