Tag: Methods

  • Mastering Large Language Models: Advanced techniques, applications, cutting-edge methods, and top LLMs (English Edition)

    Mastering Large Language Models: Advanced techniques, applications, cutting-edge methods, and top LLMs (English Edition)


    Price: $32.95
    (as of Dec 26,2024 20:25:39 UTC – Details)


    From the Publisher

    Mastering Large Language Models, New Book, BPB Publications

    Mastering Large Language Models, New Book, BPB Publications

    Author, Sanket Subhash Khandare, BPB Publications

    Author, Sanket Subhash Khandare, BPB Publications

    How does Data Quality Impact the Performance of a Language Model?

    This book lays the groundwork for understanding how data quality influences language model performance by examining the full data lifecycle, from collection to assessment. This book provides users with the information they need to develop high-quality language models by focusing on data cleaning, preprocessing, and rigorous assessment.

    What are the essential steps involved in training a language model?

    This book provides a solid foundation for understanding language model training by covering model architecture, training, and evaluation. It equips readers with the practical knowledge to navigate the complexities of data preparation, model selection, and optimization. The book’s emphasis on these fundamental steps empowers individuals to construct and refine language models effectively.

    Key topics covered

    Neural Network Architectures
    Training Techniques
    Evaluation Metrics
    NLP Applications

    What sets this book apart from the others?

    Practical Guide, LLMs, New Book, BPB Publications

    Practical Guide, LLMs, New Book, BPB Publications

    Comprehensive and Practical Guide

    This book thoroughly explores large language models, covering fundamental concepts, advanced techniques, and real-world applications. It bridges the gap between theoretical knowledge and practical implementation, making it a valuable resource for both academic researchers and industry professionals.

    Focus on practical applications, key features, BPB Publications

    Focus on practical applications, key features, BPB Publications

    Focus on Practical Applications

    This book emphasizes hands-on experience and practical implementation of large language models. It includes real-world case studies, code examples, and practical exercises to reinforce learning.

    Cutting Edge Coverage, Language Large Model, Key Features, BPB Publications

    Cutting Edge Coverage, Language Large Model, Key Features, BPB Publications

    Cutting-Edge Coverage of Language Models

    This book stays up-to-date with the latest advancements in the field of large language models, including state-of-the-art architectures and techniques.

    Ethical Consideration, LLM, Key Features, BPB Publications

    Ethical Consideration, LLM, Key Features, BPB Publications

    Emphasizing Ethical Considerations

    This book addresses the ethical implications of large language models, promoting responsible AI development and deployment. It guides mitigating biases and ensuring fairness in language models.

    RNNs, GRUs, LSTMs, Language Models, Neural Networks

    RNNs, GRUs, LSTMs, Language Models, Neural Networks

    How do neural networks, specifically RNNs, LSTMs, and GRUs, contribute to language modeling?

    RNNs handle sequential data by updating hidden states but struggle with long-term dependencies. LSTMs improve this with gating mechanisms to manage long-range context effectively, while GRUs simplify the LSTM architecture, offering similar performance with fewer parameters and reduced computational cost. Each enhances language modeling uniquely. This book also provides a strong foundation in neural networks, particularly RNNs, LSTMs, and GRUs, enabling readers to understand their role in capturing the complexities of human language.

    RoBERTa, BERT, GPT-3, Training methods of models

    RoBERTa, BERT, GPT-3, Training methods of models

    How do the architectures and training methods of models like BERT, GPT-3, and RoBERTa differ from one another?

    BERT is a bidirectional encoder model that uses Masked Language Modeling and Next Sentence Prediction to understand text deeply. GPT-3 is a unidirectional decoder model trained to generate text by predicting the next word. RoBERTa, an improved version of BERT, enhances performance by focusing solely on Masked Language Modeling and training on more data. This book comprehensively explores the architectures and training methods of prominent models such as BERT, GPT-3, and RoBERTa, providing detailed insights into their differences and functionalities.

    Publisher ‏ : ‎ BPB Publications (March 13, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 380 pages
    ISBN-10 ‏ : ‎ 9355519656
    ISBN-13 ‏ : ‎ 978-9355519658
    Item Weight ‏ : ‎ 1.44 pounds
    Dimensions ‏ : ‎ 7.5 x 0.86 x 9.25 inches


    Mastering Large Language Models: Advanced techniques, applications, cutting-edge methods, and top LLMs (English Edition)

    In this post, we will delve into the world of Large Language Models (LLMs) and explore advanced techniques, applications, cutting-edge methods, and top LLMs that are revolutionizing the field of natural language processing.

    From GPT-3 to BERT, LLMs have taken the AI world by storm with their ability to generate human-like text, answer questions, and perform a wide range of language tasks. But how can we truly master these powerful models and leverage their full potential?

    We will discuss advanced techniques for fine-tuning LLMs, optimizing performance, and overcoming common challenges in working with large language models. We will also explore innovative applications of LLMs in various industries, from healthcare to finance to entertainment.

    Additionally, we will highlight cutting-edge methods and research in the field of LLMs, including recent advancements in model architecture, training strategies, and evaluation metrics. And of course, we will showcase some of the top LLMs currently available, comparing their features, strengths, and limitations.

    Whether you are a seasoned NLP practitioner or just getting started with large language models, this post will provide valuable insights and resources to help you master the latest and greatest in the world of AI-powered language processing. Stay tuned for an in-depth exploration of LLMs and unlock their full potential!
    #Mastering #Large #Language #Models #Advanced #techniques #applications #cuttingedge #methods #top #LLMs #English #Edition

  • Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics…

    Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics…



    Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics…

    Price : 300.09

    Ends on : N/A

    View on eBay
    Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics

    Artificial neural networks (ANNs) have gained significant attention in the field of bio-/neuroinformatics due to their ability to mimic the complex structure and function of the human brain. ANNs are computational models inspired by the biological neural networks of the human brain, which are capable of learning and adapting to new information.

    In the field of bioinformatics, ANNs are being used for a variety of applications such as sequence analysis, protein structure prediction, and drug discovery. By training ANNs on large datasets of biological data, researchers are able to develop models that can accurately predict biological outcomes and identify patterns in complex biological systems.

    In neuroinformatics, ANNs are being used to study the structure and function of the brain, as well as to develop new technologies for brain-computer interfaces and neuroprosthetics. By simulating the interactions between neurons in the brain, researchers are able to gain a better understanding of how the brain processes information and controls behavior.

    Overall, artificial neural networks have shown great promise in the field of bio-/neuroinformatics and are likely to play a key role in advancing our understanding of biological systems and developing new technologies for healthcare and biotechnology.
    #Artificial #Neural #Networks #Methods #Applications #BioNeuroinformatics..

  • Workflow Management: Models, Methods, and – Paperback, by Van Der Aalst – Good

    Workflow Management: Models, Methods, and – Paperback, by Van Der Aalst – Good



    Workflow Management: Models, Methods, and – Paperback, by Van Der Aalst – Good

    Price : 6.49

    Ends on : N/A

    View on eBay
    Workflow management is a crucial aspect of any organization’s operations, ensuring efficiency and productivity. In the book “Workflow Management: Models, Methods, and – Paperback” by Van Der Aalst, readers are provided with a comprehensive guide to understanding and implementing effective workflow management strategies.

    Van Der Aalst, a renowned expert in the field, delves into various models and methods that can be utilized to streamline processes and optimize workflow. From process modeling and analysis to automation and optimization techniques, this book covers all aspects of workflow management in a clear and concise manner.

    Whether you are a business professional looking to improve your organization’s operations or a student studying business process management, this book is a valuable resource that will undoubtedly enhance your understanding of workflow management.

    Pick up a copy of “Workflow Management: Models, Methods, and – Paperback” by Van Der Aalst today and take the first step towards achieving greater efficiency and productivity in your organization.
    #Workflow #Management #Models #Methods #Paperback #Van #Der #Aalst #Good, Data Management

  • Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque (English) Ha

    Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque (English) Ha



    Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque (English) Ha

    Price : 139.71

    Ends on : N/A

    View on eBay
    Fusion Methods for Unsupervised Learning Ensembles: A Comprehensive Guide by Bruno Baruque

    Unsupervised learning ensembles have gained popularity in machine learning for their ability to combine multiple models to improve overall performance. However, one of the key challenges in unsupervised learning ensembles is how to effectively fuse the outputs of individual models to create a final prediction.

    In this post, we will explore various fusion methods for unsupervised learning ensembles, as outlined by machine learning expert Bruno Baruque. These fusion methods aim to combine the strengths of individual models while minimizing their weaknesses, ultimately leading to more accurate and robust predictions.

    Some of the fusion methods discussed by Baruque include:

    1. Clustering-based fusion: This method involves clustering the outputs of individual models and using the majority vote or a weighted voting scheme to make the final prediction. By leveraging the diversity of individual models, clustering-based fusion can improve ensemble performance.

    2. Feature-based fusion: In this approach, the features extracted by individual models are combined to create a new feature representation for the ensemble. This can help capture complementary information from different models and enhance the overall predictive power.

    3. Decision fusion: Decision fusion methods involve combining the decisions made by individual models using techniques such as averaging, stacking, or boosting. By aggregating the decisions of multiple models, decision fusion can lead to more robust and reliable predictions.

    Overall, fusion methods play a crucial role in unsupervised learning ensembles, allowing researchers and practitioners to leverage the strengths of individual models while mitigating their weaknesses. By understanding and implementing these fusion methods, we can improve the performance of unsupervised learning ensembles and unlock their full potential in various machine learning tasks.
    #Fusion #Methods #Unsupervised #Learning #Ensembles #Bruno #Baruque #English

  • Extreme Value Theory-Based Methods for Visual Recognition (Synthesis Lectures on Computer Vision)

    Extreme Value Theory-Based Methods for Visual Recognition (Synthesis Lectures on Computer Vision)


    Price: $49.99
    (as of Dec 26,2024 17:09:26 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st edition (February 15, 2017)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 132 pages
    ISBN-10 ‏ : ‎ 3031006895
    ISBN-13 ‏ : ‎ 978-3031006890
    Item Weight ‏ : ‎ 8.5 ounces
    Dimensions ‏ : ‎ 7.52 x 0.3 x 9.25 inches


    Extreme Value Theory-Based Methods for Visual Recognition (Synthesis Lectures on Computer Vision)

    Visual recognition is a crucial task in computer vision, with applications ranging from surveillance and security to autonomous driving and medical imaging. Traditional methods for visual recognition often struggle to handle extreme cases, such as rare events or outliers in the data.

    This is where Extreme Value Theory (EVT) comes in. EVT is a branch of statistics that focuses on modeling the tail behavior of extreme events in a dataset. By leveraging EVT-based methods, researchers can better capture and analyze extreme cases in visual recognition tasks, leading to more robust and reliable systems.

    In this Synthesis Lectures on Computer Vision, experts in the field explore the application of EVT-based methods to visual recognition tasks. They discuss the theoretical foundations of EVT, its relevance to computer vision, and practical implementation strategies for using EVT in visual recognition systems.

    Whether you’re a researcher, practitioner, or student in the field of computer vision, this book offers valuable insights into how EVT can enhance the performance of visual recognition systems. Stay ahead of the curve and dive into the world of Extreme Value Theory-based methods for visual recognition today.
    #Extreme #TheoryBased #Methods #Visual #Recognition #Synthesis #Lectures #Computer #Vision

  • Machine Learning Methods for Ecological Applications by Alan H. Fielding (1999,

    Machine Learning Methods for Ecological Applications by Alan H. Fielding (1999,



    Machine Learning Methods for Ecological Applications by Alan H. Fielding (1999,

    Price : 99.96

    Ends on : N/A

    View on eBay
    Machine learning methods have revolutionized ecological research in recent years, allowing scientists to analyze complex data sets and make predictions about ecosystems with unprecedented accuracy. In his seminal 1999 paper, Alan H. Fielding explores the various machine learning techniques that can be applied to ecological applications.

    Fielding begins by discussing the importance of machine learning in ecological research, highlighting its ability to uncover patterns and relationships in large and complex data sets. He goes on to outline several key machine learning methods that have been successfully used in ecological studies, including decision trees, neural networks, and support vector machines.

    One of the major advantages of machine learning in ecological applications is its ability to handle non-linear relationships and interactions between variables. This allows researchers to make more accurate predictions about how ecosystems will respond to environmental changes, such as climate change or habitat destruction.

    Fielding also emphasizes the importance of proper validation and evaluation of machine learning models in ecological research, to ensure that the predictions they generate are reliable and robust.

    Overall, Alan H. Fielding’s paper provides a comprehensive overview of the various machine learning methods that can be applied to ecological research, and highlights the potential for these techniques to significantly advance our understanding of complex ecosystems.
    #Machine #Learning #Methods #Ecological #Applications #Alan #Fielding

  • Neural Network Methods for Natural Language Processing [Synthesis Lectures on Hu

    Neural Network Methods for Natural Language Processing [Synthesis Lectures on Hu



    Neural Network Methods for Natural Language Processing [Synthesis Lectures on Hu

    Price : 24.64

    Ends on : N/A

    View on eBay
    man Language Technologies]

    In recent years, neural network methods have revolutionized the field of natural language processing (NLP). These methods have enabled significant advances in tasks such as machine translation, sentiment analysis, and speech recognition. In this post, we will explore the latest developments in neural network methods for NLP, focusing on their applications and potential impact on human language technologies.

    One of the key advantages of neural network methods for NLP is their ability to learn complex patterns and relationships in language data. Traditional NLP techniques often rely on hand-crafted rules and features, which can be labor-intensive and limited in their effectiveness. In contrast, neural networks can automatically learn from large amounts of data, allowing them to capture subtle nuances and variations in language.

    One of the most popular neural network architectures for NLP is the recurrent neural network (RNN). RNNs are well-suited for sequence modeling tasks, such as language modeling and machine translation. By processing input sequences one element at a time and maintaining a hidden state that captures context information, RNNs can effectively capture dependencies across words in a sentence or document.

    Another powerful neural network architecture for NLP is the transformer model. Transformers have gained popularity in recent years due to their ability to capture long-range dependencies in language data. By using self-attention mechanisms to weigh the importance of different words in a sequence, transformers can efficiently model relationships between distant tokens.

    In addition to architecture design, neural network methods for NLP also involve training strategies and optimization techniques. For example, techniques such as pretraining and fine-tuning have been successful in leveraging large-scale language data to improve model performance on downstream tasks. Similarly, advanced optimization algorithms like Adam and RMSprop have been instrumental in training deep neural networks more efficiently.

    Overall, neural network methods have significantly advanced the state of the art in NLP, enabling new capabilities and applications in human language technologies. As researchers continue to explore and refine these methods, we can expect further breakthroughs in areas such as conversational AI, question answering, and text generation. The synthesis lectures on human language technologies provide a comprehensive overview of these developments, offering insights into the latest trends and challenges in neural network methods for NLP.
    #Neural #Network #Methods #Natural #Language #Processing #Synthesis #Lectures

  • Statistical Methods for Speech Recognition (Language, Speech, and Communicat…

    Statistical Methods for Speech Recognition (Language, Speech, and Communicat…



    Statistical Methods for Speech Recognition (Language, Speech, and Communicat…

    Price : 5.42

    Ends on : N/A

    View on eBay
    Statistical Methods for Speech Recognition (Language, Speech, and Communication)

    Speech recognition is a fascinating field that has seen significant advancements in recent years thanks to the use of statistical methods. In this post, we will explore some of the key statistical methods that have been instrumental in improving speech recognition technology.

    One of the most commonly used statistical methods in speech recognition is Hidden Markov Models (HMMs). HMMs are a type of probabilistic model that is used to represent the temporal structure of speech signals. By modeling the transitions between different speech sounds, HMMs can accurately decode spoken words and sentences.

    Another important statistical method in speech recognition is Gaussian Mixture Models (GMMs). GMMs are used to model the acoustic features of speech signals, such as the frequency and amplitude of the sound waves. By using GMMs to represent the distribution of these features, speech recognition systems can more accurately differentiate between different phonemes and words.

    Deep learning techniques, such as neural networks, have also been increasingly used in speech recognition. By training neural networks on large amounts of speech data, these models can learn complex patterns in the acoustic signals and improve the accuracy of speech recognition systems.

    In conclusion, statistical methods play a crucial role in the development of speech recognition technology. By leveraging techniques such as HMMs, GMMs, and neural networks, researchers and engineers are able to build more accurate and robust speech recognition systems that can understand and transcribe spoken language with increasing precision.
    #Statistical #Methods #Speech #Recognition #Language #Speech #Communicat..

  • Deep Reinforcement Learning Hands-On: Apply modern RL methods to practica – GOOD

    Deep Reinforcement Learning Hands-On: Apply modern RL methods to practica – GOOD



    Deep Reinforcement Learning Hands-On: Apply modern RL methods to practica – GOOD

    Price : 31.17

    Ends on : N/A

    View on eBay
    Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems

    Are you interested in diving deep into the world of reinforcement learning and applying cutting-edge methods to real-world problems? Look no further than our hands-on workshop on Deep Reinforcement Learning.

    In this workshop, you will learn how to implement and train deep reinforcement learning algorithms such as Deep Q-Learning, Policy Gradient, and Proximal Policy Optimization. You will also learn how to apply these methods to tackle practical problems in various domains such as robotics, gaming, and finance.

    Whether you are a beginner looking to get started with reinforcement learning or an experienced practitioner looking to enhance your skills, this workshop is perfect for you. Join us and take your knowledge of reinforcement learning to the next level.

    Don’t miss out on this opportunity to learn from experts in the field and gain practical experience in applying modern reinforcement learning methods. Register now and start your journey into the exciting world of deep reinforcement learning.
    #Deep #Reinforcement #Learning #HandsOn #Apply #modern #methods #practica #GOOD

  • Networking, Models and Methods of Cloud Computing (Hardback)

    Networking, Models and Methods of Cloud Computing (Hardback)



    Networking, Models and Methods of Cloud Computing (Hardback)

    Price : 160.94

    Ends on : N/A

    View on eBay
    Networking, Models and Methods of Cloud Computing (Hardback)

    Are you interested in learning more about the intricate world of cloud computing? Look no further than this comprehensive guide on networking, models, and methods of cloud computing.

    In this hardback book, you will find detailed explanations of the various networking technologies used in cloud computing, including virtual private networks, software-defined networking, and network function virtualization. You will also explore different cloud computing models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), along with their advantages and disadvantages.

    Additionally, this book delves into the methods and strategies used in cloud computing, including virtualization, containerization, and serverless computing. You will learn how these technologies work together to provide scalable, flexible, and cost-effective solutions for businesses of all sizes.

    Whether you are a student, IT professional, or business owner looking to understand the complexities of cloud computing, this hardback book is a must-have resource. Get your copy today and unlock the potential of cloud computing for your organization.
    #Networking #Models #Methods #Cloud #Computing #Hardback, Cloud Computing

Chat Icon