Texto completo en Odilo

Tabla de Contenidos: “…La dinámica del conocimiento y de las instituciones; CAPÍTULO 2; Los cambios en el diseño institucional y la construcción de redes de modernización tecnológica; CAPÍTULO 3; Las PyMES y su importancia para la competitividad estratégica. …”

Tabla de Contenidos: “…EthernetNetwork forwarding; Using the serial console; Updating your software; The PyBBIO library; The Adafruit_BBIO library; Summary; Chapter 3: Digital Outputs; GPIO modules; Kernel drivers; Pin multiplexing; Interactive GPIO; Calculating resistor values for LEDs; Driving higher currents from GPIO pins; Blink; Taking advantage of the OS; Multiprocessing; Running at startup; Summary; Chapter 4: PWM and ADC Subsystems; PWM; Fading an LED; Servo motors; ADC; Voltage divider; Voltage follower; Your first robot; Summary; Chapter 5: User Input; Buttons; Pull-up/pull-down resistors; Polling…”

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

“…You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. …”

“…Discover how to inject your code with highly performant Rust features to develop fast and memory-safe applications Key Features Learn to implement Rust in a Python system without altering the entire system Write safe and efficient Rust code as a Python developer by understanding the essential features of Rust Build Python extensions in Rust by using Python NumPy modules in your Rust code Book Description Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. …”

“…Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. …”

“…Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. …”

“…Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. …”

“…Specific examples are provided covering a range of supported devices, sensors, and MicroPython boards such as Pycom’s WiPy modules and MicroPython’s pyboard. Never has programming for microcontrollers been easier. …”

Tabla de Contenidos: “…Building a Siri-Like Chatbot in ROS -- Social robots -- Building social robots -- Prerequisites -- Getting started with AIML -- AIML tags -- The PyAIML interpreter -- Installing PyAIML on Ubuntu 16.04 LTS -- Playing with PyAIML -- Loading multiple AIML files -- Creating an AIML bot in ROS -- The AIML ROS package -- Installing the ROS sound_play package -- Installing the dependencies of sound_play -- Installing the sound_play ROS package -- Creating the ros_aiml package -- The aiml_server node -- The AIML client node -- The aiml_tts client node -- The AIML speech recognition node -- start_chat.launch -- start_tts_chat.launch -- start_speech_chat.launch -- Questions -- Summary -- 4. …”

Tabla de Contenidos: “…-- Static and dynamic geospatial data -- Geospatial file formats -- Vector data -- Raster data -- Introducing geospatial databases and storage -- PostgreSQL and PostGIS -- ArcGIS geodatabase -- Exploring open geospatial data assets -- Human geography -- Physical geography -- Country- and area-specific data -- Summary -- Chapter 3: Working with Geographic and Projected Coordinate Systems -- Technical requirements -- Exploring geographic coordinate systems -- Understanding GCS versions -- Understanding projected coordinate systems -- Common types of projected coordinate systems -- Working with GCS and PCS in Python -- PyProj -- GeoPandas -- Summary -- Chapter 4: Exploring Geospatial Data Science Packages -- Technical requirements -- Packages for working with geospatial data -- GeoPandas -- GDAL -- Shapely -- Fiona -- Rasterio -- Packages enabling spatial analysis and modeling -- PySAL -- Packages for producing production-quality spatial visualizations -- ipyLeaflet -- Folium -- geoplot -- GeoViews -- Datashader -- Reviewing foundational data science packages -- pandas -- scikit-learn -- Summary -- Part 2: Exploratory Spatial Data Analysis -- Chapter 5: Exploratory Data Visualization -- Technical requirements -- The fundamentals of ESDA -- Example - New York City Airbnb listings -- Conducting EDA -- ESDA -- Summary -- Chapter 6: Hypothesis Testing and Spatial Randomness…”

Tabla de Contenidos: “…Limitations of using Parquet files natively in Power BI -- Using Parquet files with Python -- Analyzing Parquet data with Dask -- Analyzing Parquet data with PyArrow -- Performance differences between Dask and PyArrow -- Using Parquet files with R -- Analyzing Parquet data with Arrow for R -- Using the Parquet format to speed up a Power BI report -- Transforming historical data in Parquet -- Appending new data to and analyzing the Parquet dataset -- Analyzing Parquet data in Power BI with Python -- Analyzing Parquet data in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 11: Calling External APIs to EnrichYour Data -- Technical requirements -- What is a web service? …”

Texto completo en Odilo

Tabla de Contenidos: “…LangChain prompt templates and chains -- Key Python libraries -- pandas -- PyMongoArrow -- PyTorch -- AI/ML APIs -- OpenAI API -- Hugging Face -- Summary -- Chapter 8: Implementing Vector Search in AI Applications -- Technical requirements -- Information retrieval with MongoDB Atlas Vector Search -- Vector search tutorial in Python -- Vector Search tutorial with LangChain -- Building RAG architecture systems -- Chunking or document-splitting strategies -- Simple RAG -- Advanced RAG -- Summary -- Part 3 -- Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics -- Chapter 9: LLM Output Evaluation -- Technical requirements -- What is LLM evaluation? …”

Tabla de Contenidos: “…Creating the training loop -- Sampling techniques for deep learning models -- Random oversampling -- Dynamic sampling -- Data augmentation techniques for vision -- Data-level techniques for text classification -- Dataset and baseline model -- Document-level augmentation -- Character and word-level augmentation -- Discussion of other data-level deep learning methods and their key ideas -- Two-phase learning -- Expansive Over-Sampling -- Using generative models for oversampling -- DeepSMOTE -- Neural style transfer -- Summary -- Questions -- References -- Chapter 8: Algorithm-Level Deep Learning Techniques -- Technical requirements -- Motivation for algorithm-level techniques -- Weighting techniques -- Using PyTorch's weight parameter -- Handling textual data -- Deferred re-weighting - a minor variant of the class weighting technique -- Explicit loss function modification -- Focal loss -- Class-balanced loss -- Class-dependent temperature Loss -- Class-wise difficulty-balanced loss -- Discussing other algorithm-based techniques -- Regularization techniques -- Siamese networks -- Deeper neural networks -- Threshold adjustment -- Summary -- Questions -- References -- Chapter 9: Hybrid Deep Learning Methods -- Technical requirements -- Using graph machine learning for imbalanced data -- Understanding graphs -- Graph machine learning -- Dealing with imbalanced data -- Case study - the performance of XGBoost, MLP, and a GCN on an imbalanced dataset -- Hard example mining -- Online Hard Example Mining -- Minority class incremental rectification -- Utilizing the hard sample mining technique in minority class incremental rectification -- Summary -- Questions -- References -- Chapter 10: Model Calibration -- Technical requirements -- Introduction to model calibration -- Why bother with model calibration -- Models with and without well-calibrated probabilities…”

Tabla de Contenidos: “…Chapter 5: Simulating Semi-Rigid and Rigid Debris with Python, PyMEL, and nCloth…”

Tabla de Contenidos: “…Model collection -- Deploying a model -- Fine-tuning a model -- Creating a high-quality model with SageMaker Autopilot -- Wine quality prediction -- Setting up an Autopilot job -- Understanding an Autopilot job -- Evaluating Autopilot models -- Summary -- Further reading -- Part 3 - The Production and Operation of Machine Learning with SageMaker Studio -- Chapter 9: Training ML Models at Scale in SageMaker Studio -- Technical requirements -- Performing distributed training in SageMaker Studio -- Understanding the concept of distributed training -- The data parallel library with TensorFlow -- Model parallelism with PyTorch -- Monitoring model training and compute resources with SageMaker Debugger -- Managing long-running jobs with checkpointing and spot training -- Summary -- Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor -- Technical requirements -- Understanding drift in ML -- Monitoring data and performance drift in SageMaker Studio -- Training and hosting a model -- Creating inference traffic and ground truth -- Creating a data quality monitor -- Creating a model quality monitor -- Reviewing model monitoring results in SageMaker Studio -- Summary -- Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry -- Technical requirements -- Understanding ML operations and CI/CD -- Creating a SageMaker project -- Orchestrating an ML pipeline with SageMaker Pipelines -- Running CI/CD in SageMaker Studio -- Summary -- Index -- Other Books You May Enjoy…”