📘ADVANCED NATURAL LANGUAGE PROCESSING WITH TENSORFLOW 2: Build Real-world Effective Nlp... Applications Using Ner, Rnns, Seq2seq Models, Tran

Author(s): ASHISH. BANSAL
Publisher: Packt Publishing Limited, Year: 2021

▪️Who This Book Is For:

This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra.

The readers who can benefit the most from this book include:

Intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques
Professionals who already use TensorFlow/Python for purposes such as data science, ML, research, and analysis

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Advanced Natural Language Processing with TensorFlow

▪️What you will learn:

Grasp important pre-steps in building NLP applications like POS tagging
Deal with vast amounts of unlabeled and small labelled Datasets in NLP
Use transfer and weakly supervised learning using libraries like Snorkel
Perform sentiment analysis using BERT
Apply encoder-decoder NN architectures and beam search for summarizing text
Use transformer models with attention to bring images and text together
Build applications that generate captions and answer questions about images
Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest deep NLP models

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📘Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras

Joshua K. Cage (2020)

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Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras
Joshua K. Cage (2020)

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📘Mastering pandas for Finance Master pandas an open source Python

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Mastering pandas for Finance Master pandas an open source Python

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Learn Keras for Deep Neural Networks

Description:
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.
The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.


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Learn Keras for Deep Neural Networks

▪️What You’ll Learn
• Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions.
• Design, develop, train, validate, and deploy deep neural networks using the Keras framework
• Use best practices for debugging and validating deep learning models
• Deploy and integrate deep learning as a service into a larger software service or product
• Extend deep learning principles into other popular frameworks

▪️Who This Book Is For
Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
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Python for Probability Statist


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📘Python for Probability Statist


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کتاب یادگیری عمیق
اصول ، مفاهیم و رویکردها
تالیف و گردآوری : میلاد وزان

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📘کتاب یادگیری عمیق: اصول، مفاهیم و رویکردها
کامل‌ترین منبع فارسی یادگیری عمیق

تالیف و گردآوری : میلاد وزان

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Python: Beginner's Guide to Artificial Intelligence

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📘 Python: Beginner's Guide to Artificial Intelligence

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Data Mining for Business Analytics: Concepts, Techniques and Applications in Python

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📘Data Mining for Business Analytics: Concepts, Techniques and Applications in Python

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📘Deep Learning with JavaScript: Neural networks in TensorFlow.js


▪️Author(s): Shanqing Cai, Stan Bileschi, Eric Nielsen
Publisher: Manning Publications, Year: 2020

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Deep Learning with JavaScript: Neural networks in TensorFlow.js

▪️About the Book:
In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.

▪️What's inside :
• Image and language processing in the browser
• Tuning ML models with client-side data
• Text and image creation with generative deep learning
• Source code samples to test and modify

▪️About the Reader :
For JavaScript programmers interested in deep learning.

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📘Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code

Author(s): John T. Wolohan
Publisher: Manning, Year: 2020


Description:
Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You’ll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project.

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Mastering Large Datasets with Python

▪️About the book:
Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. You’ll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You’ll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you’ll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3.

▪️What's inside:
• An introduction to the map and reduce paradigm
• Parallelization with the multiprocessing module and pathos framework
• Hadoop and Spark for distributed computing
• Running AWS jobs to process large datasets

▪️About the reader:
For Python programmers who need to work faster with more data.


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