2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI
-
Aditi Praful Thapliyal
Abstract
The broad field of data science includes concepts associated with several artificial intelligence (AI) approaches. These include deep learning (DL) and machine learning (ML), two particularly important subfields that have transformed many sectors by enabling automation and data-driven decision-making. This chapter provides a comprehensive introduction to ML and DL, starting with their fundamental concepts. It explores various application fields in which AI has a significant influence and digs into secondary data sources that are essential for training these models. It also emphasizes the AI technologies that are commonly used in real-world applications. This chapter also discusses the difficulties faced by AI technology. Finally, it looks at AI from a future viewpoint, highlighting new developments and trends that could influence the years to come. This chapter seeks to provide readers with the information and resources necessary to navigate and participate in the rapidly changing field of AI by offering a thorough overview of ML and DL.
Abstract
The broad field of data science includes concepts associated with several artificial intelligence (AI) approaches. These include deep learning (DL) and machine learning (ML), two particularly important subfields that have transformed many sectors by enabling automation and data-driven decision-making. This chapter provides a comprehensive introduction to ML and DL, starting with their fundamental concepts. It explores various application fields in which AI has a significant influence and digs into secondary data sources that are essential for training these models. It also emphasizes the AI technologies that are commonly used in real-world applications. This chapter also discusses the difficulties faced by AI technology. Finally, it looks at AI from a future viewpoint, highlighting new developments and trends that could influence the years to come. This chapter seeks to provide readers with the information and resources necessary to navigate and participate in the rapidly changing field of AI by offering a thorough overview of ML and DL.
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
