“…This course offers a deep dive into the world of image recognition, object detection, and the magic of generative AI using tools like KerasCV, Python, TensorFlow, PyTorch, and JAX. This course guides you from the fundamentals of image classification to the intricacies of object detection, starting with the KerasCV library for deep learning. …”

“…With a focus on Data Science and Machine Learning, you'll master essential packages such as NumPy, Pandas, Matplotlib, and Scikit-learn, using the versatile Jupyter Notebook. …”

“…Vous y apprendrez à maîtriser les dernières versions de Pandas, NumPy, IPython et autre Jupyter…”

“…In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! …”

“…., anything that starts with % and is awesome); call magics, SciPy magic, and various extensions; and how IPython fits into the Jupyter ecosystem."…”

“…We'll see how a single file named size.py that calculates file sizes can make use of argparse to handle the input in the terminal. …”

“…Master Airflow installation methods like Docker and PyPI for development and production environments. …”

“…In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! …”

“…You’ll explore YAML files and use the PyYAML and tablib packages for effective manipulation. …”

“…What's Inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the Reader For machine learning practitioners who are confident in math and statistics. …”

“…In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. …”

Tabla de Contenidos: “…. -- How it works... -- Working with PyTorch tensors -- Getting ready -- How to do it... -- How it works... -- There's more... -- Contrasting fastai with Keras -- Getting ready -- How to do it... -- How it works... -- Test your knowledge -- Chapter 2: Exploring and Cleaning Up Data with fastai -- Technical requirements -- Getting the complete set of oven-ready fastai datasets -- Getting ready -- How to do it... -- How it works... -- There's more... -- Examining tabular datasets with fastai -- Getting ready -- How to do it... -- How it works... -- There's more... -- Examining text datasets with fastai -- Getting ready -- How to do it... -- How it works... -- Examining image datasets with fastai -- Getting ready -- How to do it... -- How it works... -- There's more... -- Cleaning up raw datasets with fastai -- Getting ready -- How to do it... -- How it works... -- Chapter 3: Training Models with Tabular Data -- Technical requirements -- Training a model in fastai with a curated tabular dataset -- Getting ready -- How to do it... -- How it works... -- Training a model in fastai with a non-curated tabular dataset -- Getting ready…”

Tabla de Contenidos: “…-- Learning the fundamentals of parallelism strategies -- Model parallelism -- Data parallelism -- Distributed training on PyTorch -- Basic workflow -- Communication backend and program launcher -- Quiz time! …”

Tabla de Contenidos: “…Chapter 2: Understanding Document Analysis and Creating MappingsText search; TF-IDF; Inverted indexes; Document analysis; Introducing Lucene analyzers; Creating custom analyzers; Changing a default analyzer; Putting custom analyzers into action; Elasticsearch mapping; Document metadata fields; Data types and index analysis options; Configuring data types; Indexing the same field in different ways; Putting mappings in an index; Viewing mappings; Updating mappings; Summary; Chapter 3: Putting Elasticsearch into Action; CRUD operations using elasticsearch-py; Setting up the environment…”

Tabla de Contenidos: “…Printing Observations All Printing the Observed Servers; Printing the Observed Clients; Printing the Observed Server to Client Connections; Printing a Histogram of Observations; Final P2NMAP-Analysis Script Complete Source Code; Review; Summary Questions; Additional Resource; Chapter 5 - PCAP Extractor and OS Fingerprinting; PCAP Extraction; Review of P2NMAP-Capture; Utilizing the dptk Package; P2NMAP-PCAP-Extractor.py Script; Executing P2NMAP-PCAP-Extractor; Passive OS Fingerprinting; OS Fingerprinting Truth Table; Truth Table Python Class; P2NMAP-OS-Fingerprint Script…”

Tabla de Contenidos: “…-- Installing MongoDB -- Overview of MongoDB -- MongoDB cache implementation -- Compression -- Testing the cache -- Summary -- Chapter 4: Concurrent Downloading -- One million web pages -- Parsing the Alexa list -- Sequential crawler -- Threaded crawler -- How threads and processes work -- Implementation -- Cross-process crawler -- Performance -- Summary -- Chapter 5: Dynamic Content -- An example dynamic web page -- Reverse engineering a dynamic web page -- Edge cases -- Rendering a dynamic web page -- PyQt or PySide -- Executing JavaScript -- Website interaction with WebKit -- Waiting for results -- The Render class -- Selenium -- Summary -- Chapter 6: Interacting with Forms -- The Login form -- Loading cookies from the web browser…”

Tabla de Contenidos: “…Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Regression - The Workhorse of Data Science; Regression analysis and data science; Exploring the promise of data science; The challenge; The linear models; What you are going to find in the book ; Python for data science; Installing Python; Choosing between Python 2 and Python 3; Step-by-step installation; Installing packages; Package upgrades; Scientific distributions; Introducing Jupyter or IPython; Python packages and functions for linear models ; NumPy; SciPy…”

Tabla de Contenidos: “…""Constructing a proper attribute query syntax""""Creating feature layers and table views""; ""Selecting features and rows with the Select Layer by Attribute tool""; ""Selecting features with the Select by Location tool""; ""Combining a spatial and attribute query with the Select by Location tool""; ""Chapter 8: Using the ArcPy Data Access Module with Feature Classes and Tables""; ""Introduction""; ""Retrieving features from a feature class with SearchCursor""; ""Filtering records with a where clause""; ""Improving cursor performance with geometry tokens""…”

Tabla de Contenidos: “…Choosing Between CPython, Jython, and IronPythonPyPy and Other Experimental Versions; Licensing and Price Issues; Python Development and Versions; Python Resources; Documentation; Newsgroups and Mailing Lists; Special-Interest Groups; Python Business Forum; Python Journal; Extension Modules and Python Sources; The Python Cookbook; Books and Magazines; Chapter 2. …”

Tabla de Contenidos: “…Getting Started with Delta Lake -- Getting a Standard Spark Image -- Using Delta Lake with PySpark -- Running Delta Lake in the Spark Scala Shell -- Running Delta Lake on Databricks -- Creating and Running a Spark Program: helloDeltaLake -- The Delta Lake Format -- Parquet Files -- Writing a Delta Table -- The Delta Lake Transaction Log -- How the Transaction Log Implements Atomicity -- Breaking Down Transactions into Atomic Commits -- The Transaction Log at the File Level -- Scaling Massive Metadata -- Conclusion…”