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Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to Machine Learning in Pentesting -- Technical requirements -- Artificial intelligence and machine learning -- Machine learning models and algorithms -- Supervised -- Bayesian classifiers -- Support vector machines -- Decision trees -- Semi-supervised -- Unsupervised -- Artificial neural networks -- Linear regression -- Logistic regression -- Clustering with k-means -- Reinforcement -- Performance evaluation -- Dimensionality reduction -- Improving classification with ensemble learning -- Machine learning development environments and Python libraries -- NumPy -- SciPy -- TensorFlow -- Keras -- pandas -- Matplotlib -- scikit-learn -- NLTK -- Theano -- Machine learning in penetration testing - promises and challenges -- Deep Exploit -- Summary -- Questions -- Further reading -- Chapter 2: Phishing Domain Detection -- Technical requirements -- Social engineering overview -- Social Engineering Engagement Framework -- Steps of social engineering penetration testing -- Building real-time phishing attack detectors using different machine learning models -- Phishing detection with logistic regression -- Phishing detection with decision trees -- NLP in-depth overview -- Open source NLP libraries -- Spam detection with NLTK -- Summary -- Questions -- Chapter 3: Malware Detection with API Calls and PE Headers -- Technical requirements -- Malware overview -- Malware analysis -- Static malware analysis -- Dynamic malware analysis -- Memory malware analysis -- Evasion techniques -- Portable Executable format files -- Machine learning malware detection using PE headers -- Machine learning malware detection using API calls -- Summary -- Questions -- Further reading -- Chapter 4: Malware Detection with Deep Learning…”

Tabla de Contenidos: “…Validation methods for time series -- Feature engineering for time series -- Time series forecasting as reduced regression -- Forecasting with Meta's Prophet -- AutoML for time series forecasting with PyCaret -- Summary -- Chapter 8: Multi-Factor Models -- Estimating the CAPM -- Estimating the Fama-French three-factor model -- Estimating the rolling three-factor model on a portfolio of assets -- Estimating the four- and five-factor models -- Estimating cross-sectional factor models using the Fama-MacBeth regression -- Summary -- Chapter 9: Modeling Volatility with GARCH Class Models -- Modeling stock returns' volatility with ARCH models -- Modeling stock returns' volatility with GARCH models -- Forecasting volatility using GARCH models -- Multivariate volatility forecasting with the CCC-GARCH model -- Forecasting the conditional covariance matrix using DCC-GARCH -- Summary -- Chapter 10: Monte Carlo Simulations in Finance -- Simulating stock price dynamics using a geometric Brownian motion -- Pricing European options using simulations -- Pricing American options with Least Squares Monte Carlo -- Pricing American options using QuantLib -- Pricing barrier options -- Estimating Value-at-Risk using Monte Carlo -- Summary -- Chapter 11: Asset Allocation -- Evaluating an equally-weighted portfolio's performance -- Finding the efficient frontier using Monte Carlo simulations -- Finding the efficient frontier using optimization with SciPy -- Finding the efficient frontier using convex optimization with CVXPY -- Finding the optimal portfolio with Hierarchical Risk Parity -- Summary -- Chapter 12: Backtesting Trading Strategies -- Vectorized backtesting with pandas -- Event-driven backtesting with backtrader -- Backtesting a long/short strategy based on the RSI -- Backtesting a buy/sell strategy based on Bollinger bands…”

Tabla de Contenidos: “…Weighing up your scalesThinking about paper; Defending your border; A Template for Success; Making the Most of Model Space; Setting your units; Making the drawing area snap-py (and grid-dy); Setting linetype and dimension scales; Entering drawing properties; Making Templates Your Own; Chapter 5 A Zoom with a View; Zooming and Panning with Glass and Hand; The wheel deal; Navigating a drawing; Zoom, Zoom, Zoom; A View by Any Other Name; Degenerating and Regenerating; Part 2 Let There Be Lines; Chapter 6 Along the Straight and Narrow; Drawing for Success…”

Tabla de Contenidos: “…Preparing data for a QNN -- Building the network -- Using TensorFlow with PennyLane -- Gradient computation in PennyLane -- Quantum neural networks in Qiskit: a commentary -- Summary -- Chapter 11: The Best of Both Worlds: Hybrid Architectures -- The what and why of hybrid architectures -- Hybrid architectures in PennyLane -- Setting things up -- A binary classification problem -- Training models in the real world -- A multi-class classification problem -- A general perspective on multi-class classification tasks -- Implementing a QNN for a ternary classification problem -- Hybrid architectures in Qiskit -- Nice to meet you, PyTorch! -- Setting up a model in PyTorch -- Training a model in PyTorch -- Building a hybrid binary classifier with Qiskit -- Training Qiskit QNNs with Runtime -- A glimpse into the future -- Summary -- Chapter 12: Quantum Generative Adversarial Networks -- GANs and their quantum counterparts -- A seemingly unrelated story about money -- What actually is a GAN? …”

Tabla de Contenidos: “…Using Session in a Flask app -- Connection pooling -- HTTP cache headers -- Improving data transfer -- GZIP compression -- Binary payloads -- Putting it together -- Asynchronous calls -- Task queues -- Topic queues -- Publish/subscribe -- RPC over AMQP -- Putting it together -- Testing -- Mocking synchronous calls -- Mocking asynchronous calls -- Mocking Celery -- Mocking other asynchronous calls -- Summary -- Chapter 6: Monitoring Your Services -- Centralizing logs -- Setting up Graylog -- Sending logs to Graylog -- Adding extra fields -- Performance metrics -- System metrics -- Code metrics -- Web server metrics -- Summary -- Chapter 7: Securing Your Services -- The OAuth2 protocol -- Token-based authentication -- The JWT standard -- PyJWT -- X.509 certificate-based authentication -- The TokenDealer microservice -- The POST/oauth/token implementation -- Using TokenDealer -- Web application firewall -- OpenResty - Lua and nginx -- Rate and concurrency limiting -- Other OpenResty features -- Securing your code -- Asserting incoming data -- Limiting your application scope -- Using Bandit linter -- Summary -- Chapter 8: Bringing It All Together -- Building a ReactJS dashboard -- The JSX syntax -- React components -- ReactJS and Flask -- Using Bower, npm, and Babel -- Cross-origin resource sharing -- Authentication and authorization -- Interacting with Data Service -- Getting the Strava token -- JavaScript authentication -- Summary -- Chapter 9: Packaging and Running Runnerly -- The packaging toolchain -- A few definitions -- Packaging -- The setup.py file -- The requirements.txt file -- The MANIFEST.in file -- Versioning -- Releasing -- Distributing -- Running all microservices -- Process management -- Summary -- Chapter 10: Containerized Services -- What is Docker? …”

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“…The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book focuses on Oracle Machine Learning as part of the Oracle Autonomous Database collaborative environment. …”

“…While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy—others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. …”

Tabla de Contenidos: “…-- Summary -- Chapter 2: Getting Started with Matplotlib -- Loading data -- List -- NumPy array -- pandas DataFrame -- Our first plots with Matplotlib -- Importing the pyplot -- Line plot -- Scatter plot -- Overlaying multiple data series in a plot -- Multiline plots -- Scatter plot to show clusters -- Adding a trendline over a scatter plot -- Adjusting axes, grids, labels, titles, and legends -- Adjusting axis limits -- Adding axis labels -- Adding a grid -- Titles and legends -- Adding a title -- Adding a legend -- A complete example -- Saving plots to a file -- Setting the output format…”

Tabla de Contenidos: “…Visualizing the goodness of fit -- Computing MSE and median absolute error -- Evaluating clusters with the mean silhouette coefficient -- Comparing results with a dummy classifier -- Determining MAPE and MPE -- Comparing with a dummy regressor -- Calculating the mean absolute error and the residual sum of squares -- Examining the kappa of classification -- Taking a look at the Matthews correlation coefficient -- Chapter 11: Analyzing Images -- Introduction -- Setting up OpenCV -- Applying Scale-Invariant Feature Transform (SIFT) -- Detecting features with SURF -- Quantizing colors -- Denoising images -- Extracting patches from an image -- Detecting faces with Haar cascades -- Searching for bright stars -- Extracting metadata from images -- Extracting texture features from images -- Applying hierarchical clustering on images -- Segmenting images with spectral clustering -- Chapter 12: Parallelism and Performance -- Introduction -- Just-in-time compiling with Numba -- Speeding up numerical expressions with Numexpr -- Running multiple threads with the threading module -- Launching multiple tasks with the concurrent.futures module -- Accessing resources asynchronously with the asyncio module -- Distributed processing with execnet -- Profiling memory usage -- Calculating the mean, variance, skewness, and kurtosis on the fly -- Caching with a least recently used cache -- Caching HTTP requests -- Streaming counting with the Count-min sketch -- Harnessing the power of the GPU with OpenCL -- Appendix A: Glossary -- Appendix B: Function Reference -- IPython -- Matplotlib -- NumPy -- pandas -- Scikit-learn -- SciPy -- Seaborn -- Statsmodels -- Appendix C: Online Resources -- IPython notebooks and open data -- Mathematics and statistics -- Appendix D: Tips and Tricks for Command-Line and Miscellaneous Tools -- IPython notebooks -- Command-line tools…”

Tabla de Contenidos: “…-- Setting up your shooting range -- Using PyArrow for Python -- C++ for the 1337 coders -- Go, Arrow, go! …”

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 Data Science and Python -- Chapter 1 Discovering the Match between Data Science and Python -- Understanding Python as a Language -- Viewing Python's various uses as a general-purpose language -- Interpreting Python -- Compiling Python -- Defining Data Science -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Creating the Data Science Pipeline -- Understanding Python's Role in Data Science -- Considering the shifting profile of data scientists -- Working with a multipurpose, simple, and efficient language -- Learning to Use Python Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Introducing Python's Capabilities and Wonders -- Working with Python -- Contributing to data science -- Getting a taste of the language -- Understanding the need for indentation -- Working with Jupyter Notebook and Google Colab -- Performing Rapid Prototyping and Experimentation -- Considering Speed of Execution -- Visualizing Power -- Using the Python Ecosystem for Data Science -- Accessing scientific tools using SciPy -- Performing fundamental scientific computing using NumPy -- Performing data analysis using pandas -- Implementing machine learning using Scikit-learn -- Going for deep learning with Keras and TensorFlow -- Performing analysis efficiently using XGBoost -- Plotting the data using Matplotlib -- Creating graphs with NetworkX -- Chapter 3 Setting Up Python for Data Science -- Working with Anaconda -- Using Jupyter Notebook -- Accessing the Anaconda Prompt -- Installing Anaconda on Windows -- Installing Anaconda on Linux…”

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo