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کتابخانه مهندسی کامپیوتر و پایتون

کتابخانه مهندسی کامپیوتر و پایتون

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Post #2873 2.9K
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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Post #2863 2.94K
📘Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras

Joshua K. Cage (2020)

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Post #2856 3.63K
ASHISH_BANSAL_ADVANCED_NATURAL_LANGUAGE_PROCESSING_WITH_TENSORFLOW.pdf7.1 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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Post #2855 3.24K
📘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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Post #2828 2.98K
📘Scientific Computing with Python 3

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#پایتون

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Post #2824 3.13K
📘Artificial Intelligence, IOT and machine Learning : AI programs using Python A Beginners book

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Post #2821 3.33K
🔹آموزش پیشرفته سی شارپ

مدرس : دکتر رضا مشایخی

#سی_شارپ
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Post #2811 3.25K
Gayathri_Rajagopalan_A_Python_Data_Analyst’s_Toolkit_Learn_Python.pdf7.9 MB
A Python Data Analyst’s Toolkit_ Learn Python And Python-based Libraries With Applications In Data Analysis And Statistics

▪️What You'll Learn:
• Further your programming and analytical skills with Python
• Solve mathematical problems in calculus, and set theory and algebra with Python
• Work with various libraries in Python to structure, analyze, and visualize data
• Tackle real-life case studies using Python
• Review essential statistical concepts and use the Scipy library to solve problems in statistics

▪️Who This Book Is For: Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.

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Post #2810 3.17K
📘A Python Data Analyst’s Toolkit_ Learn Python And Python-based Libraries

Author(s): Gayathri Rajagopalan
Publisher: Apress, Year: 2021

Description:
Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts – programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python – the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis.

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