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Free Learning Scrapy
Free Packt eBook by Dimitrios Kouzis-Loukas (valid through 11/17 at 19:00 EST). This book covers Scrapy, Web Scraping, Web Crawling, Apache Spark, ScrapyD, Python.
Learn the art of efficient web scraping and crawling with Python
This book covers the long awaited Scrapy v 1.0 that empowers you to extract useful data from virtually any source with very little effort. It starts off by explaining the fundamentals of Scrapy framework, followed by a thorough description of how to extract data from any source, clean it up, shape it as per your requirement using Python and 3rd party APIs. Next you will be familiarised with the process of storing the scrapped data in databases as well as search engines and performing real time analytics on them with Spark Streaming. By the end of this book, you will perfect the art of scarping data for your applications with ease.
What You Will Learn
- Understand HTML pages and write XPath to extract the data you need
- Write Scrapy spiders with simple Python and do web crawls
- Push your data into any database, search engine or analytics system
- Configure your spider to download files, images and use proxies
- Create efficient pipelines that shape data in precisely the form you want
- Use Twisted Asynchronous API to process hundreds of items concurrently
- Make your crawler super-fast by learning how to tune Scrapy’s performance
- Perform large scale distributed crawls with scrapyd and scrapinghub
About the Author
Dimitrios Kouzis-Loukas has over fifteen years experience as a topnotch software developer. He uses his acquired knowledge and expertise to teach a wide range of audiences how to write great software, as well.
He studied and mastered several disciplines, including mathematics, physics, and microelectronics. His thorough understanding of these subjects helped him raise his standards beyond the scope of “pragmatic solutions.” He knows that true solutions should be as certain as the laws of physics, as robust as ECC memories, and as universal as mathematics.
Dimitrios now develops distributed, low-latency, highly-availability systems using the latest datacenter technologies. He is language agnostic, yet has a slight preference for Python, C++, and Java. A firm believer in open source software and hardware, he hopes that his contributions will benefit individual communities as well as all of humanity.
$9.99 Building a Virtual Assistant for Raspberry Pi The practical guide for constructing a voice-controlled virtual assistant
$9.99 Apress eBook by Tanay Pant. This book covers Virtual Assistant, Raspberry Pi, Python, Speech-to-Text, Text-to-Speech.
- Develop your own voice controlled assistant, Melissa, in Python
- Learn concepts by making Melissa more intelligent as we progress along the book
- Strengthen concepts by building scalable modules for Melissa
- Discuss how Melissa can be scaled for enterprise quality products so that the learning continues
Build a voice-controlled virtual assistant using speech-to-text engines, text-to-speech engines, and conversation modules. This book shows you how to program the virtual assistant to gather data from the internet (weather data, data from Wikipedia, data mining); play music; and take notes. Each chapter covers building a mini project/module to make the virtual assistant better. You’ll develop the software on Linux or OS X before transferring it to your Raspberry Pi, ready for deploying in your own home-automation or Internet of Things applications.
Building a Virtual Assistant for Raspberry Pi walks you through various STTs and TTSs and the implementation of these components with the help of Python. After that you will start implementing logic for handling user queries and commands, so that the user can have conversations with Melissa. You will then work to improve logic handling to detect what the user wants Melissa to do. You will also work on building some useful applications/modules for Melissa, which will allow you to gain interesting information from Melissa such as the time, weather information, and data from Wikipedia.
You will develop a music playing application as well as a note taking application for Melissa, laying the foundations for how Melissa can be further extended. Finally, you will learn how to deploy this software to your Raspberry Pi and how you can further scale Melissa to make her more intelligent, interactive and how you can use her in other projects such as home automation as well.
What You’ll Learn
- Design the workflow and discover the concepts of building a voice controlled assistant
- Develop modules for having conversations with the assistant
- Enable the assistant to retrieve information from the internet
- Build utilities like a music player and a note taking application for the virtual assistant
- Integrate this software with a Raspberry Pi
Who This Book Is For
Anyone who has built a home automation project with Raspberry Pi and now want to enhance it by making it voice-controlled. The book would also interest students from computer science or related disciplines.
About the Author
Tanay Pant is an Indian author, hacker, developer and tech enthusiast. He is best known for his work on “Learning Firefox OS Application Development” which was published by Packt. He is also an official representative of Mozilla. He has been listed in the about:credits of the Firefox web browser for his contributions to the different open source projects of the Mozilla Foundation.
He also writes for a number of websites like SitePoint and Tuts+ where he shares tips and tricks about web development as well his opinions on different products. He digitally published Code Zer0 in his younger days to spread awareness about cyber security and hacker culture.
He is also the chief architect of Stock Wolf, a global virtual stock trading platform that aims to impart practical education about stocks and markets. This platform has acquired more than 100 colleges and has players from about 15 countries.
50% off Deep Learning for Search
50% off Manning’s eBook by Tommaso Teofili. This book covers Deep Learning, Neural Networks, Natural Language Processing, Apache Lucene, Deeplearning4j, Word2vec.
High-quality search is all about returning relevant results even when the data is changing or poorly structured, the queries are imprecise, and you’re trying to make sense out of images and other non-text entries. Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.
About the technology
Using deep learning and neural networks are the perfect way to create better search results, letting you fine tune what your search engines display, help speed up the results, and let you build a profile of your customers that let them find what they need every single time. And because deep learning systems improve the more you use them, your clients will also become happier in the bargain.
About the book Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. You’ll start with an overview of information retrieval principles, like indexing, searching, and ranking, as well as a fast indoctrination into deep learning. Then, you’ll move through in-depth examples as you gain an understanding of how to improve typical search tasks, such as relevance, with the help of Apache Lucene and Deeplearning4j. The book wraps up with a look at advanced problems, like searching through images and translating user queries. By the time you’re finished, you’ll be ready to build amazing search engines that deliver the results your users need and get better as time goes on!
- Applying deep learning to search
- Generating suitable synonyms
- Accurate and relevant rankings of search results
- Searching across languages
- Content-based image search
- Search with recommendations
About the reader
Written for developers comfortable with Java or a similar language. No experience with deep learning or NLP needed.
About the author
Tommaso Teofili is a software engineer at Adobe Systems with a passion for open source and artificial intelligence. He is a long-time member of the Apache Software Foundation, where he contributes to many projects on topics like information retrieval, natural language processing, and distributed computing.
50% off Relevant Search With applications for Solr and Elasticsearch
50% off Manning’s eBook by Doug Turnbull, John Berryman with foreword by Trey Grainger. This book covers Search Engines, Solr, Elasticsearch, Lucene.
Relevant Search demystifies relevance work. Using Elasticsearch, it teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines.
About the technology
Users are accustomed to and expect instant, relevant search results. To achieve this, you must master the search engine. Yet for many developers, relevance ranking is mysterious or confusing.
About the book
Relevant Search demystifies the subject and shows you that a search engine is a programmable relevance framework. You’ll learn how to apply Elasticsearch or Solr to your business’s unique ranking problems. The book demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization. In practice, a relevance framework requires softer skills as well, such as collaborating with stakeholders to discover the right relevance requirements for your business. By the end, you’ll be able to achieve a virtuous cycle of provable, measurable relevance improvements over a search product’s lifetime.
- Techniques for debugging relevance
- Applying search engine features to real problems
- Using the user interface to guide searchers
- A systematic approach to relevance
- A business culture focused on improving search
About the reader
For developers trying to build smarter search with Elasticsearch or Solr.
About the authors
Doug Turnbull is lead relevance consultant at OpenSource Connections, where he frequently speaks and blogs.
John Berryman is a data engineer at Eventbrite, where he specializes in recommendations and search.
50% off Grokking Deep Learning
50% off Manning’s eBook by Andrew W. Trask. This book covers Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Python, Gradient Descent, MNIST, Natural Language Processing.
Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the “brain” behind some of the world’s smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.
Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.
About the technology
Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the “brain” behind some of the world’s smartest Artificial Intelligence systems out there.
- How neural networks “learn”
- You will build neural networks that can see and understand images
- You will build neural networks that can translate text between languages and even write like Shakespeare
- You will build neural networks that can learn how to play videogames
About the reader
Written for readers with high school-level math and intermediate programming skills. Experience with Calculus is helpful but NOT required.
About the author
Andrew Trask is a PhD student at Oxford University, funded by the Oxford-DeepMind Graduate Scholarship, where he researches Deep Learning approaches with special emphasis on human language. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning where he trained the world’s largest artificial neural network with over 160 billion parameters, and helped guide the analytics roadmap for the Synthesys cognitive computing platform which tackles some of the most complex analysis tasks across government intelligence, finance, and healthcare industries.
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O’Reilly Media, Inc.
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