Merhaba arkadaşlar,
HuggingFace ekosistemi ile doğal dil işleme kursuna hoş geldiniz.
Bu kursta, HuggingFace içindeki Transformers, Dataset, PEFT ve Gradio gibi kütüphaneleri kullanarak doğal dil işlemeyi ve GPT ve BERT gibi büyük dil modelleri ile nasıl çalışılacağını öğreneceksiniz.
Doğal dil işleme, yapay zekanın bir alt alanı ve günümüzün en popüler alanlarından biri. ChatGPT gibi yapay zeka araçları bu alandaki teknikler kullanılarak geliştirildi.
GPT ve BERT gibi büyük dil modellerini sıfırdan eğitmek zordur. Çünkü çok fazla zaman ve para gerektirir. Şanslıyız ki son zamanlarda geliştirilen HuggingFace tarafından geliştirilen Transformers kütüphanesini kullanarak büyük dil modelleri ile çalışmak kolaylaştı.
Büyük dil modelleri 2017 yılında Google tarafından geliştirilen Transformers mimarisine dayanmaktadır. Bu mimari kullanılarak bir çok model geliştirildi. Artık çağımız bu transformer mimarisine dayalı büyük dil modellerinin çağı.
Bu modeller ile çalışmak için tek yapmanız gereken onları kendi projeniz için ayarlamak. Bu kütüphaneyi kullanarak sadece bir kaç satır kod ile metin sınırlandırma, duygu analizi, NER, soru-cevap, metin özetleme, çeviri ve metin üretme gibi projelerinizi yapabilirsiniz.
Aslında HuggingFace uçtan uca yapay zeka projelerinizi yapabildiğiniz bir platform. Bu platform ile doğal dil işleme modelinizi eğitebilir ve eğittiğiniz bu modeli canlıya alabilirsiniz.
Bu kurs, sizi HuggingFace ekosistemi ile doğal dil işleme alanında uzmanlaşmaya ve hızla büyüyen sektördeki fırsatları değerlendirmeye hazırlayacak.
Hemen kaydolun ve doğal dil işleme dünyasına dalmaya başlayın!
Kurs içinde görüşme üzere.
Tirendaz Akademi
Merhaba arkadaşlar,
HuggingFace ekosistemi ile doğal dil işleme kursuna hoş geldiniz.
Bu kursta, HuggingFace içindeki Transformers, Dataset, PEFT ve Gradio gibi kütüphaneleri kullanarak doğal dil işlemeyi ve GPT ve BERT gibi büyük dil modelleri ile nasıl çalışılacağını öğreneceksiniz.
Doğal dil işleme, yapay zekanın bir alt alanı ve günümüzün en popüler alanlarından biri. ChatGPT gibi yapay zeka araçları bu alandaki teknikler kullanılarak geliştirildi.
GPT ve BERT gibi büyük dil modellerini sıfırdan eğitmek zordur. Çünkü çok fazla zaman ve para gerektirir. Şanslıyız ki son zamanlarda geliştirilen HuggingFace tarafından geliştirilen Transformers kütüphanesini kullanarak büyük dil modelleri ile çalışmak kolaylaştı.
Büyük dil modelleri 2017 yılında Google tarafından geliştirilen Transformers mimarisine dayanmaktadır. Bu mimari kullanılarak bir çok model geliştirildi. Artık çağımız bu transformer mimarisine dayalı büyük dil modellerinin çağı.
Bu modeller ile çalışmak için tek yapmanız gereken onları kendi projeniz için ayarlamak. Bu kütüphaneyi kullanarak sadece bir kaç satır kod ile metin sınırlandırma, duygu analizi, NER, soru-cevap, metin özetleme, çeviri ve metin üretme gibi projelerinizi yapabilirsiniz.
Aslında HuggingFace uçtan uca yapay zeka projelerinizi yapabildiğiniz bir platform. Bu platform ile doğal dil işleme modelinizi eğitebilir ve eğittiğiniz bu modeli canlıya alabilirsiniz.
Bu kurs, sizi HuggingFace ekosistemi ile doğal dil işleme alanında uzmanlaşmaya ve hızla büyüyen sektördeki fırsatları değerlendirmeye hazırlayacak.
Hemen kaydolun ve doğal dil işleme dünyasına dalmaya başlayın!
Kurs içinde görüşme üzere.
Tirendaz Akademi
Many reasons exist to support the adoption of automation in drug safety surveillance. The effectiveness of artificial intelligence (AI) enables to reduce case processing costs to improve PV activities.
AI reduces human burden of repetitive tasks, increases consistency of processing, improves quality and accuracy, speed-up processing, handles diverse types of incoming data formats, allows greater focus on scientific evaluation and enables the possibility to re-analyze previous reports.
This course of "Artificial intelligence in Pharmacovigilance" is exclusively designed for pharmacovigilance professionals, pharma students, medical practitioners and life science graduates.
This course will be helpful to those who have experience in pharmacovigilance domain and for those who wants to learn role of "Artificial intelligence in Pharmacovigilance".
Automation in Pharmacovigilance is the need of hour.
Understanding the role of Artificial intelligence in Pharmacovigilance is quite complex and challenging.
The Trainer of this course has more than 11+ years of Pharmacovigilance Industry Experience from different multinational companies (MNC). He has expertise in ICSR, Aggregate Reports, Signal and Risk Management.
In this course we have covered following topics:
Introduction to Pharmacovigilance & Artificial Intelligence (AI)
Needs and Benefits of AI in Pharmacovigilance
Most identified uses of AI in Pharmacovigilance
Data processed by AI in Pharmacovigilance
Artificial Intelligence and ICSR Case processing
Artificial Intelligence and Signal Detection
By completing this course, you will get to know about the role of "Artificial intelligence in Pharmacovigilance".
So, what are you waiting for? Enroll and join us in this exciting course. See you soon in the online class.
Thanks & Regards
PV Drug Safety Academy
Many reasons exist to support the adoption of automation in drug safety surveillance. The effectiveness of artificial intelligence (AI) enables to reduce case processing costs to improve PV activities.
AI reduces human burden of repetitive tasks, increases consistency of processing, improves quality and accuracy, speed-up processing, handles diverse types of incoming data formats, allows greater focus on scientific evaluation and enables the possibility to re-analyze previous reports.
This course of "Artificial intelligence in Pharmacovigilance" is exclusively designed for pharmacovigilance professionals, pharma students, medical practitioners and life science graduates.
This course will be helpful to those who have experience in pharmacovigilance domain and for those who wants to learn role of "Artificial intelligence in Pharmacovigilance".
Automation in Pharmacovigilance is the need of hour.
Understanding the role of Artificial intelligence in Pharmacovigilance is quite complex and challenging.
The Trainer of this course has more than 11+ years of Pharmacovigilance Industry Experience from different multinational companies (MNC). He has expertise in ICSR, Aggregate Reports, Signal and Risk Management.
In this course we have covered following topics:
Introduction to Pharmacovigilance & Artificial Intelligence (AI)
Needs and Benefits of AI in Pharmacovigilance
Most identified uses of AI in Pharmacovigilance
Data processed by AI in Pharmacovigilance
Artificial Intelligence and ICSR Case processing
Artificial Intelligence and Signal Detection
By completing this course, you will get to know about the role of "Artificial intelligence in Pharmacovigilance".
So, what are you waiting for? Enroll and join us in this exciting course. See you soon in the online class.
Thanks & Regards
PV Drug Safety Academy
この講座は、プラスαの学びとして、情報学と保育学という異なる学問領域の知識を統合し、新たな視点やアプローチを得ることができることを企図した大学生・社会人向けの講座です。また、保育学をベースとして、今後、保育学の知見をAIシステムの開発や応用に活かすことで、子どもたちの学びや成長をサポートする新しい方法を追求したいという方を念頭に置いて、講座を作成しています。この講座では、ICTを活用した保育環境や教育プログラムの設計方法や実践例について取り上げ、関連する知識とスキルの向上を支援します。
この講座は、プラスαの学びとして、情報学と保育学という異なる学問領域の知識を統合し、新たな視点やアプローチを得ることができることを企図した大学生・社会人向けの講座です。また、保育学をベースとして、今後、保育学の知見をAIシステムの開発や応用に活かすことで、子どもたちの学びや成長をサポートする新しい方法を追求したいという方を念頭に置いて、講座を作成しています。この講座では、ICTを活用した保育環境や教育プログラムの設計方法や実践例について取り上げ、関連する知識とスキルの向上を支援します。
The Future is Now: A Course on Integrating AI and ChatGPT in Clinical Practice
Thank you for your consideration to join our course on "Mastering AI and ChatGPT Applications in Healthcare"
We'll delve into the fascinating world of ChatGPT and its applications in healthcare. We'll explore how this advanced language model can be harnessed to enhance patient care, support clinical decision-making, and improve the overall healthcare experience. Throughout this presentation, we'll cover various chapters that provide a comprehensive understanding of ChatGPT, natural language processing (NLP), ethical considerations, model building, integrating clinical knowledge, and more.
By the end of this session, you'll have a solid foundation to leverage ChatGPT effectively in healthcare settings, while considering important ethical and privacy concerns.
Before we dive into the technical aspects, let's briefly discuss why ChatGPT holds immense potential for healthcare professionals. The healthcare landscape is evolving rapidly, with an increasing demand for personalized care, accessible information, and efficient decision support. ChatGPT can play a pivotal role in meeting these demands. With its natural language processing capabilities, ChatGPT can engage in human-like conversations, provide accurate medical information, assist in triage and symptom assessment, and offer mental health support. It complements the expertise of healthcare professionals, enhancing patient education, streamlining administrative tasks, and delivering timely, reliable, and empathetic care.
To provide you with a structured understanding of ChatGPT in healthcare, let's take a brief look at the course outline. We'll begin with an introduction to ChatGPT and its capabilities, followed by a discussion on the ethical considerations and limitations specific to healthcare applications. We'll then delve into the technical aspects, exploring NLP techniques, model building, and the integration of clinical knowledge and guidelines. Additionally, we'll address the crucial aspects of privacy, data security, and compliance with healthcare regulations when deploying chatbots in healthcare settings. Finally, we'll discuss user experience design, evaluation metrics, and ways to continually improve chatbot models while considering bias mitigation and fairness.
The Future is Now: A Course on Integrating AI and ChatGPT in Clinical Practice
Thank you for your consideration to join our course on "Mastering AI and ChatGPT Applications in Healthcare"
We'll delve into the fascinating world of ChatGPT and its applications in healthcare. We'll explore how this advanced language model can be harnessed to enhance patient care, support clinical decision-making, and improve the overall healthcare experience. Throughout this presentation, we'll cover various chapters that provide a comprehensive understanding of ChatGPT, natural language processing (NLP), ethical considerations, model building, integrating clinical knowledge, and more.
By the end of this session, you'll have a solid foundation to leverage ChatGPT effectively in healthcare settings, while considering important ethical and privacy concerns.
Before we dive into the technical aspects, let's briefly discuss why ChatGPT holds immense potential for healthcare professionals. The healthcare landscape is evolving rapidly, with an increasing demand for personalized care, accessible information, and efficient decision support. ChatGPT can play a pivotal role in meeting these demands. With its natural language processing capabilities, ChatGPT can engage in human-like conversations, provide accurate medical information, assist in triage and symptom assessment, and offer mental health support. It complements the expertise of healthcare professionals, enhancing patient education, streamlining administrative tasks, and delivering timely, reliable, and empathetic care.
To provide you with a structured understanding of ChatGPT in healthcare, let's take a brief look at the course outline. We'll begin with an introduction to ChatGPT and its capabilities, followed by a discussion on the ethical considerations and limitations specific to healthcare applications. We'll then delve into the technical aspects, exploring NLP techniques, model building, and the integration of clinical knowledge and guidelines. Additionally, we'll address the crucial aspects of privacy, data security, and compliance with healthcare regulations when deploying chatbots in healthcare settings. Finally, we'll discuss user experience design, evaluation metrics, and ways to continually improve chatbot models while considering bias mitigation and fairness.
I. Introduction
Definition of AI and biotechnology
Brief history of AI and biotechnology
Importance of AI-powered biotech in healthcare
Objectives of the book
II. Fundamentals of AI in Biotech
Overview of AI technologies used in biotech
Machine learning algorithms in biotech
Neural networks and deep learning in biotech
Natural language processing in biotech
Image recognition and computer vision in biotech
III. Applications of AI in Biotech
Drug discovery and development
Personalized medicine
Medical imaging and diagnosis
Disease monitoring and management
Precision agriculture and food security
IV. Challenges and Opportunities of AI in Biotech
Ethical and legal implications of AI in biotech
Data privacy and security concerns in AI-powered biotech
Lack of regulatory frameworks for AI in biotech
Integration of AI and human expertise in biotech
V. Case Studies on AI-Powered Biotech
Real-world examples of AI in biotech applications
Success stories of AI-powered biotech in healthcare
Challenges faced and solutions implemented in AI-powered biotech
VI. Future Perspectives on AI in Biotech
The potential impact of AI in biotech
The future of AI-powered biotech in healthcare
Technological advancements and their potential impact on AI in biotech
New trends and opportunities for AI-powered biotech in the future
VII. Conclusion
Summary of the key points discussed in the book
Future directions for research in AI-powered biotech
Final thoughts on the potential impact of AI in biotech in the future
I. Introduction
Definition of AI and biotechnology
Brief history of AI and biotechnology
Importance of AI-powered biotech in healthcare
Objectives of the book
II. Fundamentals of AI in Biotech
Overview of AI technologies used in biotech
Machine learning algorithms in biotech
Neural networks and deep learning in biotech
Natural language processing in biotech
Image recognition and computer vision in biotech
III. Applications of AI in Biotech
Drug discovery and development
Personalized medicine
Medical imaging and diagnosis
Disease monitoring and management
Precision agriculture and food security
IV. Challenges and Opportunities of AI in Biotech
Ethical and legal implications of AI in biotech
Data privacy and security concerns in AI-powered biotech
Lack of regulatory frameworks for AI in biotech
Integration of AI and human expertise in biotech
V. Case Studies on AI-Powered Biotech
Real-world examples of AI in biotech applications
Success stories of AI-powered biotech in healthcare
Challenges faced and solutions implemented in AI-powered biotech
VI. Future Perspectives on AI in Biotech
The potential impact of AI in biotech
The future of AI-powered biotech in healthcare
Technological advancements and their potential impact on AI in biotech
New trends and opportunities for AI-powered biotech in the future
VII. Conclusion
Summary of the key points discussed in the book
Future directions for research in AI-powered biotech
Final thoughts on the potential impact of AI in biotech in the future