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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#خبر_مهم
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➕ آموزش پایتون
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🔹آموزش برنامه نویسی وب سایت های پویا با Asp.Net mvc به همراه پروژه عملی

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📘Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques (2020)

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📘Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques (2020)

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🔹آموزش پروژه محور WDF به زبان سی شارپ ( نرم افزار مدیریت فروشگاه )

مدرس: دکتر رضا مشایخی

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📘Transformers for Natural Language Processing (2021)

The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.
The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.


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Transformers for Natural Language Processing (2021)

▪️What you will learn

Use the latest pretrained transformer models

Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models

Create language understanding Python programs using concepts that outperform classical deep learning models

Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP

Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more

Measure the productivity of key transformers to define their scope, potential, and limits in production

▪️Who this book is for
Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.

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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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📍آموزش OpenCV با انجام پروژه قلم مجازی

#پردازش_تصویر #OpenCV
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📘Machine Learning in Java


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