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Tag: Transformers
Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models
Price: $36.54
(as of Dec 25,2024 11:06:33 UTC – Details)
ASIN : B0CTHPCMQ6
Publisher : Academic Press; 3rd edition (December 6, 2024)
Publication date : December 6, 2024
Language : English
File size : 90604 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Enabled
Print length : 1194 pages
Page numbers source ISBN : 0443292388
Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion ModelsMachine learning has come a long way since its inception, evolving from classic algorithms like linear regression and decision trees to more advanced models such as deep neural networks, transformers, and diffusion models.
The classic machine learning algorithms, such as linear regression and decision trees, laid the foundation for modern machine learning techniques. These algorithms are still widely used today for tasks such as regression, classification, and clustering. However, as data and computational power have grown, more complex models have emerged.
Deep neural networks, inspired by the structure of the human brain, have revolutionized machine learning. These networks are capable of learning intricate patterns in data and have been successfully applied to tasks such as image and speech recognition, natural language processing, and autonomous vehicles.
Transformers, introduced in 2017, have further pushed the boundaries of machine learning. These models are based on a self-attention mechanism, allowing them to process sequences of data more efficiently than traditional recurrent neural networks. Transformers have been instrumental in advancements in natural language processing, with models like BERT and GPT-3 achieving state-of-the-art performance on various tasks.
Diffusion models, a recent development in machine learning, leverage the principles of physics to model complex systems. These models have shown promise in generating high-quality images, videos, and text, as well as in solving challenging optimization problems.
As machine learning continues to advance, it is essential to stay updated on the latest developments and techniques. From the classics to deep networks, transformers, and diffusion models, there is a vast array of tools and algorithms available to tackle a wide range of tasks. By understanding and leveraging these models, we can unlock the full potential of machine learning in various applications.
#Machine #Learning #Classics #Deep #Networks #Transformers #Diffusion #Models
Natural Language Processing Practical using Transformers with Python: Building Language Applications with Small Projects
Price: $9.99
(as of Dec 25,2024 10:56:44 UTC – Details)
ASIN : B0DG5PTSK5
Publication date : September 4, 2024
Language : English
File size : 1491 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 338 pages
Natural Language Processing (NLP) has become an essential tool in various industries, from customer service to healthcare. With the rise of transformer models like BERT, GPT-3, and T5, building language applications has become more accessible and powerful than ever before. In this post, we will explore how to use transformers with Python to build practical NLP applications through small projects.Project 1: Text Classification with BERT
In this project, we will use BERT (Bidirectional Encoder Representations from Transformers) to classify text into different categories. We will start by fine-tuning a pre-trained BERT model on a text classification dataset and then evaluate its performance on a test set. By the end of this project, you will have a working text classification model that can be used for various applications, such as sentiment analysis or topic categorization.Project 2: Text Generation with GPT-3
In this project, we will leverage OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) model to generate text based on a given prompt. We will interact with the GPT-3 API using Python and experiment with different prompts to see how the model generates text. By the end of this project, you will have a basic understanding of how to use GPT-3 for text generation and creative writing tasks.Project 3: Question Answering with T5
In this project, we will use Google’s T5 (Text-to-Text Transfer Transformer) model to build a question-answering system. We will fine-tune the T5 model on a question-answering dataset and then test its performance on a set of questions. By the end of this project, you will have a question-answering system that can provide accurate and relevant answers to user queries.By working on these small projects, you will gain hands-on experience with transformers and learn how to apply them to real-world NLP tasks. Whether you are a beginner or an experienced developer, these projects will help you enhance your NLP skills and build powerful language applications using Python. So, roll up your sleeves and let’s get started with Natural Language Processing Practical using Transformers with Python!
#Natural #Language #Processing #Practical #Transformers #Python #Building #Language #Applications #Small #Projects
Natural Language Processing with Transformers: Building Language Applications with Hugging Face
Price: $64.78
(as of Dec 24,2024 19:53:25 UTC – Details)
From the brand
Browse our NLP & LLM books
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
ASIN : 1098103246
Publisher : O’Reilly Media; 1st edition (March 1, 2022)
Language : English
Paperback : 406 pages
ISBN-10 : 9355420323
ISBN-13 : 978-9355420329
Item Weight : 1.55 pounds
Dimensions : 7 x 1 x 9.25 inchesCustomers say
Customers find the book’s content excellent and well-explained. They appreciate the clear explanations of intuition behind transformer models. Readers also mention that the color figures are accurate, even though the print version is black and white.
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Natural Language Processing (NLP) has seen incredible advancements in recent years, thanks in large part to the development of Transformer models. These models, like BERT, GPT-3, and T5, have revolutionized the field of NLP by significantly improving the performance of tasks like text classification, language translation, and question answering.One of the leading platforms for working with Transformer models is Hugging Face. Hugging Face provides a wide range of pre-trained Transformer models that can be easily integrated into your language applications. Whether you’re building a chatbot, sentiment analysis tool, or language translation service, Hugging Face has the tools you need to get started.
In this post, we’ll explore how you can leverage Hugging Face’s Transformer models to build powerful language applications. We’ll cover topics like fine-tuning pre-trained models, using pipelines for common NLP tasks, and deploying your models in production environments.
If you’re interested in diving into the world of NLP with Transformers, stay tuned for our upcoming posts on Natural Language Processing with Transformers: Building Language Applications with Hugging Face. Let’s unlock the full potential of NLP together!
#Natural #Language #Processing #Transformers #Building #Language #Applications #Hugging #Face
Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using tensorflow and keras
Price: $39.99
(as of Dec 24,2024 16:29:44 UTC – Details)
From the Publisher
Neural Networks with Python
Chapters You Must Read..
Python, TensorFlow, and your First Neural Network
Deep Dive into Feedforward Networks
Convolutional Networks for Visual Tasks
Recurrent Networks for Sequence Data
Data Generation with GANs
Transformers for Complex Tasks
Autoencoders for Data Compression and Generation
Capsule NetworksPower of Python, TensorFlow and Keras to build strong Deep Leaning Models
What’s unique about this book is that we’ll also focus on the problems you might face while building these networks. We’ll look at how to troubleshoot them and even how to fine-tune your models. By the end of it, you won’t just know how to build a neural network; you’ll know what to do when things don’t go as planned.
If you’re up for a rollercoaster ride through the incredible world of neural networks, hold tight. Grab a cup of coffee, open up your favorite code editor, and let’s get started.
Gain flexibility with diverse neural network architectures for various problems.
Hands-on experience in building, training, and fine-tuning neural networks.
Learn strategic approaches for troubleshooting and optimizing neural models.
Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.
Acquire skills in crucial data preprocessing and augmentation techniques.
Understand and apply optimization techniques and hyperparameter tuning.
Implement an end-to-end machine learning project, from data to deployment.
Master Python for machine learning, from setup to complex models.
ASIN : B0CMNYBF2Q
Publisher : GitforGits; 1st edition (November 3, 2023)
Publication date : November 3, 2023
Language : English
File size : 955 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 152 pages
Page numbers source ISBN : 8119177487
Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using tensorflow and kerasIn this post, we will delve into the world of Neural Networks and explore the various architectures that can be designed using Python, specifically with the help of tensorflow and keras libraries.
Convolutional Neural Networks (CNNs) are widely used in image recognition tasks, and we will cover how to design and train CNNs using tensorflow and keras. We will also discuss how to fine-tune pre-trained models for specific tasks.
Transformers have gained popularity in natural language processing tasks, and we will explore how to implement transformers for tasks such as text classification and language translation.
Generative Adversarial Networks (GANs) are used for generating new data samples, and we will cover how to design and train GANs using tensorflow and keras. We will also discuss how to evaluate the performance of GANs.
Capsule networks are a relatively new architecture that has shown promise in tasks such as image classification and object detection. We will explore how to design and train capsule networks using tensorflow and keras.
By the end of this post, you will have a comprehensive understanding of how to design and train various neural network architectures using tensorflow and keras in Python. Stay tuned for more in-depth tutorials and practical examples!
#Neural #Networks #Python #Design #CNNs #Transformers #GANs #capsule #networks #tensorflow #keras
Transformers for Natural Language Processing – Second Edition: Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI’s GPT-3, ChatGPT, and GPT-4
Price: $89.99
(as of Dec 24,2024 15:47:27 UTC – Details)
From the brand
Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.
Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.
We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.
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Publisher : Packt Publishing; 2nd ed. edition (March 25, 2022)
Language : English
Paperback : 602 pages
ISBN-10 : 1803247339
ISBN-13 : 978-1803247335
Item Weight : 2.25 pounds
Dimensions : 7.5 x 1.22 x 9.25 inchesCustomers say
Customers find the book informative and comprehensive, with detailed code that allows for hands-on experience. They describe the content as good and a great resource.
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Are you ready to dive deeper into the world of Transformers for Natural Language Processing? Look no further than the second edition of our comprehensive guide, where we will show you how to build, train, and fine-tune deep neural network architectures for NLP using Python, Hugging Face, and OpenAI’s latest models – GPT-3, ChatGPT, and the upcoming GPT-4.In this updated edition, we will cover advanced topics such as transfer learning, domain adaptation, and model interpretability, giving you the tools you need to take your NLP projects to the next level. Whether you are a seasoned NLP practitioner or just getting started, this book will provide you with the knowledge and skills to harness the power of Transformers for Natural Language Processing.
Stay tuned for the release date and get ready to revolutionize your NLP workflows with the latest advancements in deep learning technology. Transformers for NLP – Second Edition is coming soon!
#Transformers #Natural #Language #Processing #Edition #Build #train #finetune #deep #neural #network #architectures #NLP #Python #Hugging #Face #OpenAIs #GPT3 #ChatGPT #GPT4
Natural Language Processing with Transformers: Fundamentals and Core Applications: A Practical Guide. From Beginner to Intermediate in Building Intelligent Language Applications
Price: $34.90
(as of Dec 24,2024 14:24:06 UTC – Details)
Natural Language Processing (NLP) with Transformers has revolutionized the way we work with textual data. In this practical guide, we will dive deep into the fundamentals of Transformers and explore their core applications in building intelligent language applications. Whether you are a beginner or an intermediate developer, this guide will help you understand the key concepts and techniques behind NLP with Transformers.Transformers have gained popularity in recent years due to their ability to process large amounts of text data efficiently and accurately. By utilizing attention mechanisms, Transformers can capture long-range dependencies in text and generate more meaningful representations of language. This has led to significant advancements in tasks such as text classification, sentiment analysis, machine translation, and question-answering.
In this guide, we will start by introducing the basics of NLP and the Transformer architecture. We will then explore how to implement and fine-tune pre-trained Transformer models, such as BERT, GPT-2, and T5, using popular libraries like Hugging Face’s Transformers. We will also cover techniques for handling text data preprocessing, tokenization, and model evaluation.
Throughout the guide, we will work on hands-on projects that demonstrate the practical applications of Transformers in NLP. By the end of this guide, you will have the knowledge and skills to build intelligent language applications, such as chatbots, text summarization tools, and sentiment analysis systems.
Whether you are looking to enhance your NLP skills or embark on a new project, this guide will equip you with the necessary tools and techniques to succeed in the field of Natural Language Processing with Transformers. Let’s embark on this exciting journey together and unlock the potential of intelligent language applications.
#Natural #Language #Processing #Transformers #Fundamentals #Core #Applications #Practical #Guide #Beginner #Intermediate #Building #Intelligent #Language #Applications
PyTorch for Generative AI: A Practical Guide to GANs, Diffusion, and Transformers for Realistic AI Generation
Price: $16.99
(as of Dec 24,2024 12:05:26 UTC – Details)
Generative AI has gained immense popularity in recent years, with techniques like Generative Adversarial Networks (GANs), Diffusion Models, and Transformers leading the way in generating realistic and high-quality AI-generated content. One of the most popular frameworks for implementing these techniques is PyTorch, a deep learning library that provides a flexible and efficient platform for building and training neural networks.In this practical guide, we will explore how PyTorch can be used to implement state-of-the-art generative AI models, including GANs, Diffusion Models, and Transformers. We will walk through the implementation of each of these models, providing code examples and step-by-step instructions to help you get started with generative AI using PyTorch.
GANs, which consist of a generator and a discriminator network, have been widely used for generating realistic images, videos, and text. We will show you how to implement a GAN in PyTorch, training it on a dataset of your choice to generate new and realistic content.
Diffusion Models, on the other hand, are a powerful class of generative models that can generate high-quality samples by iteratively refining a noise input. We will demonstrate how to implement a diffusion model in PyTorch, allowing you to generate realistic and diverse samples with ease.
Finally, we will explore how PyTorch can be used to implement Transformers, a versatile architecture that has been successful in generating text, images, and music. We will provide a detailed walkthrough of implementing a Transformer model in PyTorch, enabling you to generate realistic and coherent AI-generated content.
Whether you are a beginner or an experienced deep learning practitioner, this guide will equip you with the knowledge and tools needed to harness the power of PyTorch for generative AI. By the end of this guide, you will have a solid understanding of how to implement GANs, Diffusion Models, and Transformers in PyTorch, opening up a world of possibilities for realistic AI generation.
#PyTorch #Generative #Practical #Guide #GANs #Diffusion #Transformers #Realistic #Generation
Generative AI in C++: Coding Transformers and LLMs (Generative AI LLM Programming)
Price: $19.95
(as of Dec 24,2024 04:28:16 UTC – Details)
ASIN : B0D14LHGZ6
Publisher : Independently published (April 8, 2024)
Language : English
Paperback : 766 pages
ISBN-13 : 979-8871928684
Item Weight : 2.76 pounds
Dimensions : 6 x 1.73 x 9 inches
Generative AI in C++: Coding Transformers and LLMs (Generative AI LLM Programming)Generative AI, particularly Language Models (LMs) like GPT-3, have gained immense popularity in recent years for their ability to produce human-like text based on input prompts. One of the most widely used frameworks for developing such models is OpenAI’s GPT-3, which is based on the Transformer architecture.
If you’re interested in delving into the world of generative AI and building your own Language Models in C++, this post is for you. In this post, we’ll explore the basics of coding Transformers and Large Language Models (LLMs) in C++.
Transformers are a type of deep learning model that excels at capturing long-range dependencies in sequential data, making them ideal for tasks like natural language processing. LLMs, on the other hand, are a specific type of Transformer model that has been pre-trained on vast amounts of text data and fine-tuned for specific tasks.
To get started with coding Transformers and LLMs in C++, you’ll need a good understanding of deep learning concepts, particularly neural networks and attention mechanisms. You’ll also need to be familiar with libraries like TensorFlow or PyTorch, which provide the necessary tools for building and training deep learning models.
As you delve deeper into the world of generative AI programming, you’ll discover the endless possibilities that these models offer, from generating text for chatbots and virtual assistants to creating realistic dialogue for video games and movies.
So, if you’re ready to dive into the exciting world of generative AI in C++, start by learning the basics of coding Transformers and LLMs. With a little practice and experimentation, you’ll be well on your way to building your own cutting-edge Language Models that can generate human-like text with just a few lines of code.
#Generative #Coding #Transformers #LLMs #Generative #LLM #Programming
Transformers R.E.D. Optimus Prime Robot Enhanced Design Exclusive Figure RARE
Transformers R.E.D. Optimus Prime Robot Enhanced Design Exclusive Figure RARE
Price : 25.00
Ends on : N/A
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Are you a die-hard Transformers fan? Do you love collecting exclusive figures? Well, you’re in luck because the Transformers R.E.D. Optimus Prime Robot Enhanced Design Exclusive Figure is here and it is RARE!This limited edition figure features an incredibly detailed design of the iconic Autobot leader, Optimus Prime. With intricate sculpting and stunning paintwork, this figure truly captures the essence of the beloved character.
The Robot Enhanced Design (R.E.D.) line is known for its high-quality collectibles that cater to adult fans of Transformers. This exclusive Optimus Prime figure is a must-have for any serious collector.
Don’t miss out on the opportunity to add this rare and highly sought-after figure to your collection. Get your hands on the Transformers R.E.D. Optimus Prime Robot Enhanced Design Exclusive Figure before it’s too late!
#Transformers #R.E.D #Optimus #Prime #Robot #Enhanced #Design #Exclusive #Figure #RARE
Natural Language Processing with Transformers, Revised Edition – VERY GOOD
Natural Language Processing with Transformers, Revised Edition – VERY GOOD
Price : 36.24
Ends on : N/A
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Natural Language Processing with Transformers, Revised Edition – VERY GOODAre you ready to take your natural language processing skills to the next level? Look no further than the revised edition of “Natural Language Processing with Transformers.” This updated version offers even more comprehensive coverage of the latest advancements in NLP, with a focus on the powerful capabilities of transformer models.
Transformers have revolutionized the field of NLP, enabling breakthroughs in tasks such as language translation, text generation, and sentiment analysis. This revised edition delves deep into the inner workings of transformers, providing a clear and concise explanation of how these models operate and how they can be leveraged for a wide range of NLP tasks.
Whether you’re a seasoned NLP practitioner looking to expand your knowledge or a newcomer eager to learn the ropes, “Natural Language Processing with Transformers” has something for everyone. The revised edition includes updated code examples, hands-on exercises, and real-world applications to help you master the concepts and techniques of transformer-based NLP.
Don’t miss out on this opportunity to enhance your NLP skills and stay ahead of the curve. Get your hands on the revised edition of “Natural Language Processing with Transformers” today and take your NLP capabilities to new heights.
#Natural #Language #Processing #Transformers #Revised #Edition #GOOD
