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47 tulosta hakusanalla Taweh Beysolow II

Introduction to Deep Learning Using R

Introduction to Deep Learning Using R

Taweh Beysolow II

APress
2017
nidottu
Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive powerWho This Book Is ForStudents, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
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Applied Natural Language Processing with Python

Applied Natural Language Processing with Python

Taweh Beysolow II

APress
2018
nidottu
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment.What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and GensimManipulate and preprocess raw text data in formats such as .txt and .pdfStrengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.
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Applied Reinforcement Learning with Python

Applied Reinforcement Learning with Python

Taweh Beysolow II

APress
2019
nidottu
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll LearnImplement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
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Raweh D'Awahathan Taweh

Raweh D'Awahathan Taweh

Samir Johna

Amz Book Publishing Solutions
2024
pokkari
This is the first original book of (Raweh) poetry resembling Haiku in Assyrian? Aramaic language. It addressed all aspects of life with as reflected upon by the author. Raweh is form of a sung poetry. The entire poem is one stanza of three lines, each line must be completed in 7 syllables only. There are very few documented data and publications about Raweh. The Assyrians in the Mountains of Hakkari would get together and recite each stanza in a strong mountaineer voice with no music. Topics were about war and love. This form of poetry goes back for decades although we do not know the exact timing. The only other nation that share this kind of poetry that is very close to Raweh is Japan (Haiku), which also comes in 3 lines but 5,7,5 syllables in each stanza. I always wonder if there is any link between the two nations through this kind of poetry. This is my humble attempt to preserve and revive this form of poetry, expanding it into areas not cited before such as social sciences, nature, economy, science, history, religion, and politics.
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Who by Fire, Who by Water - Un'Taneh Tokef

Who by Fire, Who by Water - Un'Taneh Tokef

Jewish Lights Publishing
2014
nidottu
The most controversial prayer of the Jewish New Year what it means, who wrote it, why we say it. New in paperback Explore the profound, perplexing and persuasive power of Un'taneh Tokef, one of the most beloved, prominent and controversial pieces in the Ashkenazi High Holy Day liturgy. Interact with thought-provoking and inspiring discussions on all aspects of this prayer that defies easy understanding its moral challenge, fatalistic theology, proclamation of God's holiness, call to human responsibility and prescription for redemption. Commentaries from over forty men and women, scholars and rabbis, artists and poets from all major Jewish denominations examine Un'taneh Tokef from the viewpoints of the ancient Rabbis and modern theologians, as well as halakhic, Talmudic, linguistic, biblical, mystical, feminist, community and personal perspectives. For a complete list of contributors, see www.jewishlights.com.
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