“…Hours of in-depth AI tutorials including the UC Berkeley RiseLab team on building reinforcement learning applications with Ray; the Microsoft Cloud AI Group on how to apply recurrent neural networks to time series forecasting; and former Think Big Analytics AI practice director Mo Patel on computer vision and PyTorch. Keynotes covering the challenges of AI from Supasorn Suwajanakorn (Independent), Ruchir Puri (IBM), Jonathan Ballon (Intel), Ashok Srivastava (Intuit), and others. …”

“…You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics u..…”

“…One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. …”

“…What You Will Learn Understand the intuition behind anomalies Convert your intuition into technical descriptions of anomalous data Detect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile range Apply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysis Work with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearn Develop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series data Who This Book Is For For software developers with at least some familiarity with the Python programming language, and who would like to understand the science and some of the statistics behind anomaly detection techniques. …”

“…In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. …”

“…Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. …”

“…As you learn to implement a deep Q-learning algorithm to play the CartPole game, you'll gain hands-on experience with PyTorch. Finally, you'll explore ways to run machine learning models in production as part of an AI application. …”

“…Data Science and Machine Learning: 60+ sessions delivered by data scientists from Teradata, UC Berkeley RISE Lab, Microsoft, and more on the technologies that discover the hidden insights in your data, includes tutorials on building PyTorch based recommender systems; using R and Python for scalable data science, machine learning, and AI; and how to get started with TensorFlow. …”

“…What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. …”

“…Some session of note include: Bayesian Statistics Made Simple Gradient Descent, Demystified Comparing Models Using Resampling and Bayesian Methods Next Generation Indexes For Big Data Engineering Probabilistic Programming with PyMC3 Racial Bias in Facial Recognition Software Visualization throughout the Data Science Workflow: Datafy All The Things Data Science, Management..…”

“…Each of the conference's 17 keynote sessions are included, as well as all of the 167 specialized sessions, covering topics such as PyTorch, a flexible and intuitive framework for deep learning; Docker on Yarn; Spark structured streaming; the Netflix data platform; RubiX, a caching framework f..…”

“…What you will learn Discover the optimal approach for loading and managing data Work with notebooks for various tasks, including ML Run real-time analytics using Azure Synapse Link for Cosmos DB Perform exploratory data analytics using Apache Spark Read and write DataFrames into Parquet files using PySpark Create reports on various metrics for monitoring key KPIs Combine Power BI and Serverless for distributed analysis Enhance your Synapse analysis with data visualizations Who this book is for This book is for data architects, data engineers, and developers who want to learn and understand the main concepts of Azure Synapse analytics and implement them in real-world scenarios…”

“…What you will learn Use Python's libraries to avoid the complexities of state-changing classes Leverage built-in higher-order functions to avoid rewriting common algorithms Write generator functions to create lazy processing Design and implement decorators for functional composition Make use of Python type annotations to describe parameters and results of functions Apply functional programming to concurrency and web services Explore the PyMonad library for stateful simulations Who this book is for The functional paradigm is very useful for programmers working in data science, but any Python developer who wants to create more reliable, succinct, and expressive code will have much to learn from this book. …”

“…The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. …”

“…Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). …”

“…One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. …”

“…One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. …”

“…Unter Einsatz von Python und der Bibliothek PyTorch ermöglicht Ihnen der Autor so einen einfachen und praktischen Einstieg in die Konzepte und Methoden des Reinforcement Learnings wie Deep Q-Networks, Wertiteration, Policy Gradients, Trust Region Policy Optimization (TRPO), genetische Algorithmen und viele mehr. …”

“…Get your Raspberry Pi and choose the right low-cost peripherals Set up Raspian Linux and the Python programming environment Learn Python basics, including arithmetic and structured commands Master Python 3 lists, tuples, diction-aries, sets, strings, files, and modules Reuse the same Python code in multiple locations with functions Manipulate string data efficiently with regular expressions Practice simple object-oriented programming techniques Use exception handling to make your code more reliable Program modern graphical user interfaces with Raspberry Pi and OpenGL Create Raspberry Pi games with the PyGame library Learn network, web, and database techniques you can..…”

“…You'll be able to learn about all of these advances in sessions, such as Jason Knight (Intel) on Intel's Nervana Graph project (a universal deep learning compiler); Ion Stoica's (UC Berkeley) on Ray, a new distributed execution framework for reinforcement learning applications; Mary Wahl (Microsoft) on scalable operationalization of trained CNTK and TensorFlow DNNs; and Jeremy Howard (fast.ai) on using GPU acceleration with PyTorch to make your algorithms 2,000% faster. But the real foci of the conference were the AI implementation sessions and this compilation lets you listen in on all of them. …”