Explaining Neural Networks In Raw Python
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Explaining Neural Networks In Raw Python
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Author : Wojciech Broniowski
language : en
Publisher: Wojciech Broniowski
Release Date : 2021-07-15
Explaining Neural Networks In Raw Python written by Wojciech Broniowski and has been published by Wojciech Broniowski this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Computers categories.
These lectures explain the very basic concepts of neural networks at a most elementary level, requiring only very rudimentary knowledge of Python, or actually any programming language. With simplicity in mind, the code for various algorithms of neural networks is written from absolute scratch, i.e. without any use of dedicated higher-level libraries. That way one can follow all the programming steps in an explicit manner. The book is intended for undergraduate students and for advanced high school pupils and their teachers.
Modern Data Mining With Python
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Author : Dushyant Singh Sengar
language : en
Publisher: BPB Publications
Release Date : 2024-02-26
Modern Data Mining With Python written by Dushyant Singh Sengar and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Computers categories.
Data miner’s survival kit for explainable, effective, and efficient algorithms enabling responsible decision-making KEY FEATURES ● Accessible, and case-based exploration of the most effective data mining techniques in Python. ● An indispensable guide for utilizing AI potential responsibly. ● Actionable insights on modeling techniques, deployment technologies, business needs, and the art of data science, for risk mitigation and better business outcomes. DESCRIPTION "Modern Data Mining with Python" is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards. After reading this book, readers will be equipped with the skills and knowledge necessary to use Python for data mining and analysis in an industry set-up. They will be able to analyze and implement algorithms on large structured and unstructured datasets. WHAT YOU WILL LEARN ● Explore the data mining spectrum ranging from data exploration and statistics. ● Gain hands-on experience applying modern algorithms to real-world problems in the financial industry. ● Develop an understanding of various risks associated with model usage in regulated industries. ● Gain knowledge about best practices and regulatory guidelines to mitigate model usage-related risk in key banking areas. ● Develop and deploy risk-mitigated algorithms on self-serve ModelOps platforms. WHO THIS BOOK IS FOR This book is for a wide range of early career professionals and students interested in data mining or data science with a financial services industry focus. Senior industry professionals, and educators, trying to implement data mining algorithms can benefit as well. TABLE OF CONTENTS 1. Understanding Data Mining in a Nutshell 2. Basic Statistics and Exploratory Data Analysis 3. Digging into Linear Regression 4. Exploring Logistic Regression 5. Decision Trees with Bagging and Boosting 6. Support Vector Machines and K-Nearest Neighbors 7. Putting Dimensionality Reduction into Action 8. Beginning with Unsupervised Models 9. Structured Data Classification using Artificial Neural Networks 10. Language Modeling with Recurrent Neural Networks 11. Image Processing with Convolutional Neural Networks 12. Understanding Model Risk Management for Data Mining Models 13. Adopting ModelOps to Manage Model Risk
Building Conversational Generative Ai Apps With Langchain And Gpt
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Author : Mugesh S
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-06-04
Building Conversational Generative Ai Apps With Langchain And Gpt written by Mugesh S and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-04 with Computers categories.
TAGLINE Transform Text into Intelligent Conversations with LangChain and GPT. KEY FEATURES ● Build AI Chatbots with LangChain, Python and GPT models through hands-on guidance. ● Master Advanced Techniques like RAG, document embedding, and LLM fine-tuning. ● Deploy and Scale conversational AI systems for real-world applications. DESCRIPTION Conversational AI Apps are revolutionizing the way we interact with technology, enabling businesses and developers to create smarter, more intuitive applications that engage users in natural, meaningful ways. Building Conversational Generative AI Apps with LangChain and GPT is your ultimate guide to mastering AI-driven conversational systems. Starting with core concepts of generative AI and LLMs, you'll learn to build intelligent chatbots and virtual assistants, while exploring techniques like fine-tuning LLMs, retrieval-augmented generation (RAG), and document embedding. As you progress, you'll dive deeper into advanced topics such as vector databases and multimodal capabilities, enabling you to create highly accurate, context-aware AI agents. The book's step-by-step tutorials ensure that you develop practical skills in deploying and optimizing scalable conversational AI solutions. By the end, you'll be equipped to build AI apps that enhance customer engagement, automate workflows, and scale seamlessly. Unlock the potential of Building Conversational Generative AI Apps with LangChain and GPT and create next-gen AI applications today! WHAT WILL YOU LEARN ● Build and deploy AI-driven chatbots using LangChain and GPT models. ● Implement advanced techniques like retrieval-augmented generation (RAG) for smarter responses. ● Fine-tune LLMs for domain-specific conversational AI applications. ● Leverage vector databases for efficient knowledge retrieval and enhanced chatbot performance. ● Explore multimodal capabilities and document embedding for better context-aware responses. ● Optimize and scale conversational AI systems for large-scale deployments. WHO IS THIS BOOK FOR? This book is for developers, data scientists, and AI enthusiasts eager to build conversational applications using LangChain and GPT models. While a basic understanding of Python and machine learning concepts is beneficial, the book offers step-by-step guidance, making it accessible to both beginners and experienced practitioners. TABLE OF CONTENTS 1. Introduction to Conversational Generative AI 2. Natural Language Processing (NLP) Fundamentals 3. The Building Blocks of Rule-Based Chatbots 4. Statistical Language Models for Text Generation 5. Neural Network Architectures for Conversation 6. The Transformer Architecture Revolution 7. Unveiling ChatGPT and Architectures 8. Langchain Framework for Building Conversational AI 9. Exploring the LLM Landscape beyond GPT 10. The Transformative Impact of Conversational AI 11. Challenges and Opportunities in LLM Development Index
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-20
Python Machine Learning written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-20 with Computers categories.
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
Natural Language Processing With Python
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Author : Dr. Bharti Salunke
language : en
Publisher: Xoffencerpublication
Release Date : 2024-11-06
Natural Language Processing With Python written by Dr. Bharti Salunke and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-06 with Computers categories.
Natural Language Processing (NLP) is a rapidly evolving field within artificial intelligence that focuses on the interaction between computers and human languages. It is concerned with the ability of machines to read, understand, and generate human language in a way that is both meaningful and contextually relevant. The integration of NLP with Python has revolutionized this domain, as Python's simplicity, versatility, and extensive libraries make it an ideal tool for developing NLP applications. This abstract delves into the essential aspects of NLP using Python, exploring key concepts, tools, and techniques that enable machines to process and analyze large amounts of natural language data. At its core, NLP involves several fundamental tasks, including tokenization, part-of-speech tagging, named entity recognition, syntactic parsing, and sentiment analysis. Python, with its rich ecosystem of libraries such as NLTK, spaCy, and transformers, provides an accessible and robust framework for tackling these tasks. Tokenization, for instance, breaks down text into smaller units such as words or sentences, which forms the foundation for many NLP applications. Part-of-speech tagging assigns grammatical labels to words, while named entity recognition identifies specific entities like names, dates, or locations within the text. Syntactic parsing helps in understanding the grammatical structure of sentences, and sentiment analysis enables machines to determine the emotional tone of a piece of text. One of the significant advancements in NLP is the application of machine learning techniques to language processing. Python’s libraries such as scikit-learn, TensorFlow, and PyTorch offer powerful tools for training models that can predict and classify language data. Deep learning models, particularly those based on neural networks, have led to major breakthroughs in tasks like machine translation, speech recognition, and question answering. Pre-trained models like BERT and GPT, implemented through Python frameworks, have set new benchmarks in NLP, allowing developers to build more sophisticated and accurate systems with minimal training data.
Mastering Nlp From Foundations To Llms
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Author : Lior Gazit
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-04-26
Mastering Nlp From Foundations To Llms written by Lior Gazit and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Computers categories.
Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key Features Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT Master embedding techniques and machine learning principles for real-world applications Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDo you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.What you will learn Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python Model and classify text using traditional machine learning and deep learning methods Understand the theory and design of LLMs and their implementation for various applications in AI Explore NLP insights, trends, and expert opinions on its future direction and potential Who this book is for This book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.
Neural Networks Tricks Of The Trade
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Author : Grégoire Montavon
language : en
Publisher: Springer
Release Date : 2012-11-14
Neural Networks Tricks Of The Trade written by Grégoire Montavon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-14 with Computers categories.
The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
Artificial Neural Networks In Food Processing
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Author : Mohamed Tarek Khadir
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2021-01-18
Artificial Neural Networks In Food Processing written by Mohamed Tarek Khadir and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-18 with Technology & Engineering categories.
Artificial Neural Networks (ANNs) is a powerful computational tool to mimic the learning process of the mammalian brain. This book gives a comprehensive overview of ANNs including an introduction to the topic, classifications of single neurons and neural networks, model predictive control and a review of ANNs used in food processing. Also, examples of ANNs in food processing applications such as pasteurization control are illustrated.
Mastering Your Prompt Engineering Super Power
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Author : Diana Ashcroft
language : en
Publisher: Athena Publishing
Release Date :
Mastering Your Prompt Engineering Super Power written by Diana Ashcroft and has been published by Athena Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
In a world driven by data and powered by artificial intelligence, there's a superpower that's changing the game: Prompt Engineering. Join Diana Ashcroft, a seasoned data scientist and educator, on a journey through the dynamic landscape of prompt engineering in her latest book, Mastering Your Prompt Engineering Super Power. Prompt engineering is the key to unlocking the full potential of AI. In Mastering Your Prompt Engineering Super Power, Diana Ashcroft delves into the heart of this transformative field and reveals its immense significance. You'll discover how prompt engineering is reshaping industries, powering innovation, and shaping the future of society. Whether you're a seasoned AI professional or just starting your journey, Mastering Your Prompt Engineering Super Power is your guide to mastering prompt engineering. Diana takes complex concepts and distills them into practical, down-to-earth knowledge that anyone can grasp. You'll explore the realms of Natural Language Processing (NLP), Computer Vision, and more, gaining the skills needed to harness prompt engineering's incredible potential. Prompt engineering isn't just a buzzword; it's a force that's driving change in every sector. Diana provides real-world examples of how prompt engineering is making waves in industries like healthcare, finance, e-commerce, and beyond. You'll see how AI-powered prompts are enhancing productivity, improving customer experiences, and even revolutionizing education. Mastering Your Prompt Engineering Super Power isn't just a book; it's your passport to becoming a prompt engineering master. Diana guides you through hands-on techniques, tools, and frameworks used by professionals in the field. You'll learn to wield the power of AI-driven prompts to tackle complex tasks, from data preprocessing to model optimization. As we stand on the precipice of a new era, Diana Ashcroft illuminates the path forward. Discover how prompt engineering is shaping the future, from enabling smarter virtual assistants to aiding legal professionals in document analysis. The possibilities are endless, and Mastering Your Prompt Engineering Super Power equips you to seize them.
Kickstart Artificial Intelligence Fundamentals Master Machine Learning Neural Networks And Deep Learning From Basics To Build Modern Ai Solutions With Python And Tensorflow Keras
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Author : Dr. S.Mahesh
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-03-29
Kickstart Artificial Intelligence Fundamentals Master Machine Learning Neural Networks And Deep Learning From Basics To Build Modern Ai Solutions With Python And Tensorflow Keras written by Dr. S.Mahesh and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-29 with Computers categories.
Master AI Fundamentals and Build Real-World Machine Learning and Deep Learning Solutions. Key Features● Hands-on AI guide with Python, TensorFlow, and Keras implementations.● Step-by-step walkthroughs of Machine Learning, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models.● Bridges AI theory with real-world applications and coding exercises. Book DescriptionAI is transforming industries, driving innovation, and shaping the future of technology. A strong foundation in AI fundamentals is essential for anyone looking to stay ahead in this rapidly evolving field. Kickstart Artificial Intelligence Fundamentals is a comprehensive companion designed to demystify core AI concepts, covering Machine Learning, Deep Learning, and Neural Networks. Tailored for all AI enthusiasts, this book provides hands-on Python implementation using the TensorFlow-Keras framework, ensuring a seamless learning experience from theory to practice. Bridging the gap between concepts and real-world applications, this book offers intuitive explanations, mathematical foundations, and practical use cases. Readers will explore supervised and unsupervised Machine Learning models, master Convolutional Neural Networks for image classification, and leverage Long Short-Term Memory networks for time-series forecasting. Each chapter includes coding examples and guided exercises, making it an invaluable resource for both beginners and advanced learners. Beyond technical expertise, this book explores emerging trends like Generative AI and ethical considerations in AI, preparing readers for the challenges and opportunities in the field. This book will provide you the essential knowledge and hands-on experience to stay competitive. Don’t get left behind—embrace AI and future-proof your career today! What you will learn● Build and train machine learning models for real-world datasets.● Apply neural networks to classification and regression tasks.● Implement CNNs and LSTMs for vision and sequence modeling.● Solve AI problems using Python, TensorFlow, and Keras.● Fine-tune pre-trained models for domain-specific applications.● Explore generative AI for creative and industrial use cases.