Deep-Learning-with-PyTorch-Quick-Start-Guide.pdf
6.4 MB
Deep Learning with PyTorch Quick Start Guide
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AI_for_Data_Science_Artificial_Intelligence_Frameworks_and_Functionality.pdf
1.9 MB
AI for Data Science
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📘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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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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#یادگیری_ماشین #علم_داده #تنسورفلو #پردازش_زبان_طبیعی
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ASHISH_BANSAL_ADVANCED_NATURAL_LANGUAGE_PROCESSING_WITH_TENSORFLOW.pdf
7.1 MB
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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▪️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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#یادگیری_ماشین #علم_داده #تنسورفلو #پردازش_زبان_طبیعی
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📘Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras
Joshua K. Cage (2020)
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Joshua K. Cage (2020)
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Python_Data_Analysis_for_Newbies_Numpypandasmatplotlibscikit_learnkeras.pdf
3.1 MB
Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras
Joshua K. Cage (2020)
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Joshua K. Cage (2020)
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Mastering_pandas_for_Finance_Master_pandas,_an_open_source_Python.pdf
7 MB
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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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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[Jojo_John_Moolayil]_Learn_Keras_for_Deep_Neural_N.pdf
2.7 MB
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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▪️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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(by-Jos-Unpingco)-Python-for-Probability,-Statist.pdf
11.1 MB
📘Python for Probability Statist
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کتاب یادگیری عمیق
اصول ، مفاهیم و رویکردها
تالیف و گردآوری : میلاد وزان
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اصول ، مفاهیم و رویکردها
تالیف و گردآوری : میلاد وزان
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یادگیری عمیق.pdf
6.7 MB
📘کتاب یادگیری عمیق: اصول، مفاهیم و رویکردها
کامل‌ترین منبع فارسی یادگیری عمیق
تالیف و گردآوری : میلاد وزان
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کامل‌ترین منبع فارسی یادگیری عمیق
تالیف و گردآوری : میلاد وزان
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Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
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Data Mining for Business Analytics (en).pdf
13 MB
📘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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▪️Author(s): Shanqing Cai, Stan Bileschi, Eric Nielsen
Publisher: Manning Publications, Year: 2020
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#تنسورفلو #یادگیری_عمیق #جاوااسکریپت
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Deep_Learning_with_JavaScript_Neural_networks_in_TensorFlow_js_by.pdf
12.6 MB
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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▪️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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#تنسورفلو #یادگیری_عمیق #جاوااسکریپت
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