“…In addition to the wide variety of technologies ML engineers must learn (including TensorFlow, PyTorch, AWS, Azure, BigQuery and many others), they have to deal with challenges like lack of data or data that's poorly labeled, fit, or collected to begin with. …”

“…Znalazł się tu dokładny opis IPythona, NumPy, Pandas, Matplotlib, Scikit-Learn i innych narzędzi. …”

Tabla de Contenidos: “…; Which Django Version to use; Starting the project; Summary; Chapter 3: Models; M is bigger than V and C; The model hunt; Splitting models.py into multiple files; Structural patterns; Patterns -- normalized models; Problem details; Solution details; Three steps of normalization; First normal form (1NF); Second normal form (2NF); Third normal form (3NF).; Django models; Performance and denormalization; Should we always normalize?…”

Tabla de Contenidos: “…7: Hosting Untrusted Users Under Xen: Lessons from the TrenchesAdvantages for the Users; Shared Resources and Protecting Them from the Users; Tuning CPU Usage; Scheduling for Providers; Controlling Network Resources; Storage in a Shared Hosting Environment; Regulating Disk Access with ionice; Backing Up DomUs; Remote Access to the DomU; An Emulated Serial Console; A Menu for the Users; PyGRUB, a Bootloader for DomUs; Making PyGRUB Work; Wrap-Up; 8: Beyond Linux: Using Xen with Other Unix-like OSs; Solaris; Getting Started with Solaris; Solaris Dom0; Setting Up Xen; Solaris SMF…”

Tabla de Contenidos: “…-A Python Answer; Using PyGeoIP to Correlate IP to Physical Locations; Using Dpkt to Parse Packets; Using Python to Build a Google Map; Is Anonymous Really Anonymous? …”

Tabla de Contenidos: “…; Starting the project; Summary; Chapter 3: Models; M is bigger than V and C; The model hunt; Splitting models.py into multiple files; Structural patterns; Patterns: normalized models; Problem details; Solution details; Pattern: Model mixins; Problem details; Solution details; Pattern: User profiles; Problem details; Solution details; Pattern: Service objects…”

Tabla de Contenidos: “…Project 1 - Installing an advanced audio playback applicationBuilding an Internet radio based on VLC and Raspberry Pi; Project 2a - Running VLC in the background for the Internet radio; Starting VLC automatically at reboot; Project 2b - Designing a playlist file for the Internet radio; Chapter 4 supplemental materials; Project 2c: Parsing the playlist file for the Internet radio; Project 2d - Implementing a Python text interface to VLC; Project 3 - Implementing a TKinter GUI for the Internet radio; Running tktest.py; Creating a clock in the radio UI; Running tkradio.py…”

Tabla de Contenidos: “…LoggerWriting the code; Code walk-through; Examining main-code walk-through; ParseCommandLine(); ValiditingDirectoryWritable; WalkPath; HashFile; CSVWriter; Full code listing pfish.py; Full code listing _pfish.py; Results presentation; Chapter review; Summary questions; Looking ahead; Additional Resources; Chapter 4: Forensic Searching and Indexing Using Python; Introduction; Keyword context search; How can this be accomplished easily in Python?…”

Tabla de Contenidos: “…Chapter 3: Working with Robot Simulation Using ROS and GazeboUnderstanding robotic simulation; Mathematical modeling of the robot; Introduction to the differential steering system and robot kinematics; Explaining of the forward kinematics equation; Inverse kinematics; Introduction to ROS and Gazebo; ROS Concepts; Installing ROS Indigo on Ubuntu 14.04.2; Introducing catkin; Creating an ROS Package; Hello_world_publisher.py; Hello_world_subscriber.py; Introducing Gazebo; Installing Gazebo; Testing Gazebo with the ROS interface; Installing TurtleBot Robot packages on ROS Indigo…”

Tabla de Contenidos: “…-- Understanding the advantages of the cloud -- Installing Python -- Installing PyCharm -- Creating a new project -- Summary -- Chapter 2: Creating an AWS Account -- Creating an AWS account -- Summary -- Part 2: A Deep Dive into AWS with Python -- Chapter 3: Cloud Computing with Lambda -- Cloud computing -- What is Lambda? …”

Tabla de Contenidos: “…Working with Vectors, Matrices, and Arrays in NumPy -- 1.0 Introduction -- 1.1 Creating a Vector -- Problem -- Solution -- Discussion -- See Also -- 1.2 Creating a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.3 Creating a Sparse Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.4 Preallocating NumPy Arrays -- Problem -- Solution -- Discussion -- 1.5 Selecting Elements -- Problem…”

Tabla de Contenidos: “…Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark - predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place - Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels - Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up - Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one - the perceptron -- Combining perceptrons - a single-layer neural network -- Parameter fitting with back-propagation…”

Tabla de Contenidos: “…. -- See also -- Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib -- Using pandas to process vaccine-adverse events -- Getting ready -- How to do it... -- There's more... -- See also -- Dealing with the pitfalls of joining pandas DataFrames -- Getting ready -- How to do it... -- There's more... -- Reducing the memory usage of pandas DataFrames -- Getting ready -- How to do it... -- See also -- Accelerating pandas processing with Apache Arrow -- Getting ready -- How to do it... -- There's more... -- Understanding NumPy as the engine behind Python data science and bioinformatics -- Getting ready -- How to do it... -- See also -- Introducing Matplotlib for chart generation -- Getting ready -- How to do it... -- There's more... -- See also -- Chapter 3: Next-Generation Sequencing -- Accessing GenBank and moving around NCBI databases -- Getting ready -- How to do it... -- There's more... -- See also -- Performing basic sequence analysis -- Getting ready -- How to do it... -- There's more... -- See also -- Working with modern sequence formats -- Getting ready -- How to do it... -- There's more... -- See also -- Working with alignment data -- Getting ready -- How to do it... -- There's more... -- See also -- Extracting data from VCF files -- Getting ready -- How to do it... -- There's more... -- See also…”

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Texto completo en Odilo

Texto completo en Odilo

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Texto completo en Odilo

Tabla de Contenidos:

Tabla de Contenidos: “…2.1.33 CREACIÓN DE LAS TABLAS -- 2.1.34 SCRIPT DE LA TABLA PRODUCTOS -- 2.1.35 ESTRUCTURA RECOMENDADA -- 2.1.36 CREACIÓN DE NUESTRA PRIMERA APLICACIÓN -- 2.1.37 ESTRUCTURA DE LA APP: ALMACÉN -- 2.1.38 CONFIGURACIÓN DE LAS APPS -- 2.1.39 MODIFICANDO LA VISTA: VIEWS.PY Y (...) -- 2.1.40 MODIFICANDO VIEWS.PY -- 2.1.41 MODIFICANDO LA URLS.PY DEL PROYECTO1 (...) -- 2.1.42 MEJORANDO LA VISTA -- 2.1.43 LENGUAJE DE PLANTILLA DE DJANGO -- 2.1.44 CREACIÓN DE TEMPLATES -- 2.1.45 AÑADIENDO LAS PLANTILLAS -- 2.1.46 CONFIGURACIÓN DE LA CARPETA TEMPLATE -- 2.1.47 MODIFICANDO LA URLS.PY DEL PROYECTO1 (...) -- 2.1.48 CONFIGURACIÓN DEL ARCHIVO SETTING.PY -- 2.1.49 COPIAR A LA CARPETA STATIC -- 2.1.50 CONFIGURAMOS LA URL DEL PROYECTO1 -- 2.1.51 CREAMOS EL ARCHIVO URLS DE LA APP (...) -- 2.1.52 NAVEGANDO APPS ALMACEN -- 2.1.53 CREANDO EL CRUD DE PRODUCTOS -- 2.1.54 DENTRO DE CADA APPS SE DEBERÁ CREAR (...) -- 2.1.55 EJECUTAMOS LA APPS -- 2.2 CONFIGURACIÓN DE SALIDA DE URL LOCAL (...) -- CAPÍTULO 3. …”