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Generative AI Specialization
Unlocking Boundless Creativity: Exploring the Depths of Generative AI for Revolutionary Applications. Become elite Generative AI Engineer/Researcher.Preview Generative AI Specialization course
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Course/Topic - Generative AI Specialization - all lectures
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Lecture 1 - Introduction to Generative AI - part 1
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Lecture 2 - Introduction to Generative AI - part 2
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Lecture 3 - Introduction to Generative AI - part 3
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Lecture 4 - Introduction to Large Language Models (LLMs) - part 1
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Lecture 5 - Introduction to Large Language Models (LLMs) - part 2
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Lecture 6 - Prompt Engineering Basics - part 1
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Lecture 7 - Prompt Engineering Basics - part 2
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Lecture 8 - Responsible AI
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Lecture 9 - Generative AI - Impact - Considerations - Ethical Issues
The course objectives of a Generative AI course are:
1) Understanding Fundamentals: Gain a solid understanding of the fundamental concepts underlying generative AI, including probabilistic modeling, deep learning, and neural networks.
2) Algorithm Understanding: Learn about various generative AI algorithms such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models, understanding their principles, architectures, and applications.
3) Hands-on Experience: Develop practical skills through hands-on programming assignments and projects using popular deep learning frameworks such as TensorFlow or PyTorch.
4) Creative Applications: Explore the creative potential of generative AI for tasks such as image generation, text generation, music generation, and beyond.
5) Prompt Engineering: Learn how to design effective prompts for generative AI systems, considering factors such as clarity, specificity, and relevance to the desired output.
6) Ethical Considerations: Discuss the ethical implications of generative AI technologies, including issues related to bias, fairness, privacy, and misuse, and learn how to develop responsible AI systems.
7) Research Literacy: Develop the ability to read and critically evaluate research papers in the field of generative AI, and potentially conduct original research.
8) Real-world Applications: Explore real-world applications of generative AI across various domains such as art, design, healthcare, and gaming.
9) Performance Evaluation: Learn how to evaluate the performance of generative AI models using appropriate metrics and techniques.
10) Collaboration and Communication: Enhance collaboration and communication skills through group projects and presentations, fostering the ability to work effectively in interdisciplinary teams.
These objectives aim to provide you with a comprehensive understanding of generative AI concepts, practical skills, and ethical considerations, preparing you for both further study and application in industry or research.
Generative AI Fundamentals Specialization - Course Curriculum
Introduction to Generative AI
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What is Generative AI?
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Journey of Generative AI
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How does Generative AI works?
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Applications of generative AI in different sectors and industries
Introduction to Large Language Models (LLM)
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What is LLM?
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How do large language models work?
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General Architecture of Large Language Model
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What can a language model do?
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What are the challenges and limitations of LLM?
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LLM in the AI landscape
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LLM use cases/Application
Generative AI: Prompt Engineering Basics
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What is prompt Engineering?
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Relevance of prompt engineering in generative AI models
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Creating prompts and explore examples of impactful prompts
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Commonly used tools for prompt engineering to aid with prompt engineering
Introduction to Responsible AI
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What is Responsible AI?
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Why it's important?
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How Google implements responsible AI in their products?
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Google's 7 AI principles
Generative AI: Impact, Considerations, and Ethical Issues
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Limitations of generative AI and the related concerns
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Identify the ethical issues, concerns, and misuses associated with generative AI
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Considerations for the responsible use of generative AI
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Economic and social impact of generative AI
Generative AI is a new field and the certifications in this area are emerging. The considerations can be for:
1) Vendor-specific certifications: Some cloud platforms, such as Google Cloud and Microsoft Azure, offer introductory courses and certifications on generative AI. These certifications focus on using the generative AI capabilities of the specific platform.
2) Independent certifications: A few independent organizations offer certifications in generative AI. These certifications typically cover a broader range of topics, but they may not be as widely recognized as vendor-specific certifications.
3) Course completions: Many universities and online learning platforms offer courses on generative AI. While not technically certifications, completing these courses can demonstrate your knowledge and skills in the field.
Some specific examples of certifications and courses available in generative AI:
1. Google Cloud: Introduction to Generative AI Learning Path and Generative AI for Developers Learning Path
2. Microsoft Azure: Introduction to Generative AI
3. Chartered Institute of Professional Certifications: Certified Generative AI Specialist (CGAI™)
4. Uplatz: Generative AI Fundamentals Course Completion Certificate by Uplatz
Generative AI, with its ability to create novel content and data, offers a promising and exciting career prospect for individuals with the right skills and interests. Here's a breakdown of its scope and potential:
Career Scope
1. Diverse Applications: Generative AI finds applications across various industries, including:
a) Creative content generation: Designing, writing, composing music, generating marketing materials.
b) Drug discovery and materials science: Accelerating research and development processes.
c) Personalized experiences: Tailoring products, services, and recommendations to individual preferences.
d) AI-assisted design and engineering: Optimizing designs, generating prototypes, and automating tasks.
e) Data augmentation and synthesis: Creating realistic synthetic data for training and development purposes.
2. New Job Roles: Generative AI sparks the creation of new job roles, such as:
a) Generative AI engineer: Developing and implementing generative models.
b) AI-assisted content creator: Working alongside AI to produce high-quality content.
c) Creative technologist: Blending creativity with technical expertise to leverage AI tools.
d) Data scientist for generative AI: Analyzing and manipulating data for model training.
e) Ethical AI specialist: Ensuring responsible development and use of generative models.
Job Prospects
1. High Demand: The demand for skilled professionals in generative AI is rapidly growing, exceeding the supply in many regions.
2. Competitive Salaries: Due to the demand and specialized skills required, salaries in generative AI tend to be competitive.
3. Growth Potential: Generative AI is still in its early stages, presenting abundant opportunities for professional growth and advancement.
4. Remote Work Flexibility: Many generative AI roles are well-suited for remote work, offering flexibility and global career opportunities.
However, it's important to consider:
1. Emerging Field: Generative AI is still evolving, and specific job roles and requirements might change over time.
2. Continuous Learning: Keeping up with advancements and upskilling yourself is crucial for staying relevant in this fast-paced field.
3. Ethical Considerations: Understanding and addressing the ethical implications of using generative AI is becoming increasingly important.
The career scope and job prospects in generative AI are promising for individuals who are passionate about technology, creativity, and innovation. With the right skills and continuous learning, this field offers exciting opportunities for a fulfilling and rewarding career.
These questions and answers cover a broad range of topics in generative AI, from foundational concepts to advanced techniques and applications, and can serve as a comprehensive resource for interview preparation.
1. What is Generative AI?
Generative AI refers to a class of algorithms and models in artificial intelligence that are capable of generating new data samples similar to those in the training data distribution.
2. Can you explain the difference between discriminative and generative models?
Discriminative models learn the boundary between classes in the data, while generative models learn the joint probability distribution of the input features and the labels.
3. What are some common applications of generative AI?
Some common applications include image generation, text generation, music generation, data augmentation, and synthetic data generation.
4. What is a Generative Adversarial Network (GAN)?
A GAN is a type of generative model that consists of two neural networks, a generator and a discriminator, which are trained simultaneously through a min-max game to generate realistic data samples.
5. How does the training process of a GAN work?
The generator generates fake data samples, and the discriminator distinguishes between real and fake samples. They are trained iteratively, with the generator aiming to produce samples that are indistinguishable from real data and the discriminator improving its ability to distinguish between real and fake samples.
6. What are some challenges associated with training GANs?
Mode collapse, training instability, and vanishing gradients are some common challenges. Mode collapse occurs when the generator produces limited diversity in generated samples.
7. Explain the concept of mode collapse in GANs.
Mode collapse happens when the generator of a GAN fails to capture the diversity of the data distribution and instead learns to produce a limited set of samples, often resulting in low-quality outputs.
8. What are some techniques used to mitigate mode collapse in GANs?
Techniques like minibatch discrimination, feature matching, and adding noise to the input can help mitigate mode collapse.
9. What is the purpose of the Wasserstein distance in Wasserstein GANs?
The Wasserstein distance provides a more stable training objective compared to traditional GANs, resulting in more stable training and higher-quality sample generation.
10. What are autoencoders, and how are they used in generative modeling?
Autoencoders are neural networks trained to reconstruct their input. Variational autoencoders (VAEs), a type of autoencoder, are used in generative modeling to learn a latent space representation of the data distribution, allowing for the generation of new samples.
11. What distinguishes VAEs from traditional autoencoders?
VAEs learn a probabilistic latent space representation, allowing for sampling and interpolation between data points. Traditional autoencoders typically learn a deterministic representation.
12. What is the difference between VAEs and GANs?
VAEs learn the underlying probability distribution of the data, while GANs learn to generate samples through a min-max game between a generator and a discriminator.
13. Explain the concept of latent space in generative modeling.
Latent space is a lower-dimensional representation of the data learned by a generative model, where each point in the space corresponds to a possible data sample.
14. What role does the encoder play in a variational autoencoder (VAE)?
The encoder maps input data to a probabilistic distribution in the latent space, allowing for sampling from the learned distribution.
15. What is the KL divergence in VAEs, and why is it used?
KL divergence measures the difference between two probability distributions. In VAEs, it is used to ensure that the learned latent space distribution is close to a predefined prior distribution, typically a Gaussian distribution.
16. How do you evaluate the quality of generated samples in generative models?
Common metrics include visual inspection, inception score, Fréchet Inception Distance (FID), and perceptual similarity metrics.
17. What is adversarial training, and how is it used in generative modeling?
Adversarial training involves training two neural networks in a min-max game, such as in GANs, where one network generates data samples (the generator), and the other network evaluates the samples (the discriminator).
18. Can you explain the concept of style transfer in generative modeling?
Style transfer involves transferring the style of one image onto another while preserving its content, often achieved using techniques like neural style transfer or conditional generative models.
19. What is the difference between unconditional and conditional generative models?
Unconditional generative models generate samples without any conditioning information, while conditional generative models generate samples conditioned on additional input information.
20. How do you handle missing data in generative modeling?
Techniques such as data imputation using generative models or conditional generation conditioned on available data can be used to handle missing data.
21. What is the role of attention mechanisms in generative modeling?
Attention mechanisms allow models to focus on different parts of the input data, improving performance in tasks such as image generation or language modeling.
22. Explain the concept of self-attention in generative models.
Self-attention allows a model to weigh the importance of different input elements when generating an output, enabling better capture of long-range dependencies and relationships.
23. What are some ethical considerations in generative AI?
Ethical considerations include the potential for misuse of generated content, biases present in training data, and implications for privacy and consent when generating or manipulating data.
24. How can generative models be used for data augmentation?
Generative models can be trained on existing data to generate additional synthetic data samples, which can then be used to augment the training dataset and improve model performance.
25. What are some challenges in deploying generative models in real-world applications?
Challenges include model scalability, computational resources required for inference, and potential biases or ethical concerns in generated outputs.
26. What techniques can be used to ensure diversity in generated samples?
Techniques such as incorporating diversity-promoting objectives during training or using ensemble methods can help ensure diversity in generated samples.
27. How do you handle class imbalance in generative modeling tasks?
Techniques such as class-aware sampling during training or incorporating class balancing objectives can help address class imbalance in generative modeling tasks.
28. Explain the concept of multimodal generation in generative models.
Multimodal generation involves generating diverse outputs for a given input, capturing multiple plausible interpretations or variations of the input data.
29. What are some examples of multimodal generation tasks?
Examples include image captioning, where multiple captions can describe the same image, or artistic style transfer, where multiple artistic styles can be applied to the same input image.
30. How can generative models be used for anomaly detection?
Generative models can be trained on normal data samples and used to generate new samples. Anomalies are then identified as data points that deviate significantly from the generated samples.
31. What role does reinforcement learning play in generative modeling?
Reinforcement learning can be used to train generative models with explicit rewards or objectives, allowing for more fine-grained control over the generated outputs.
32. Can you explain the concept of transfer learning in the context of generative modeling?
Transfer learning involves leveraging knowledge from pre-trained models to improve performance on new tasks or datasets. In generative modeling, transfer learning can involve fine-tuning pre-trained models or using pre-trained components in new architectures.
33. How do you handle scalability issues when training large-scale generative models?
Techniques such as distributed training, model parallelism, or data parallelism can be used to scale training to large datasets or model sizes.
34. What are some techniques for generating high-resolution images with generative models?
Progressive growing techniques, hierarchical architectures, or attention mechanisms can be used to generate high-resolution images with generative models.
35. Explain the concept of controllable generation in generative models.
Controllable generation refers to the ability to control specific attributes or characteristics of the generated outputs, such as style, content, or semantics.
36. What are some methods for incorporating controllability into generative models?
Methods such as conditional generation, attribute manipulation, or disentangled representations can be used to incorporate controllability into generative models.
37. How do you evaluate the interpretability of generative models?
Interpretability can be evaluated through techniques such as feature visualization, attribution methods, or human evaluations of generated outputs.
38. What are some applications of interpretable generative models?
Applications include medical image analysis, where interpretable models can provide insights into the underlying factors contributing to disease progression, or natural language processing, where interpretable models can aid in understanding model decisions.
39. Explain the concept of unsupervised representation learning in generative models.
Unsupervised representation learning involves learning meaningful representations of data without explicit supervision, often through techniques such as autoencoders or generative adversarial networks.
40. What are some techniques for improving sample quality in generative models?
Techniques such as curriculum learning, adversarial training, or progressive growing can be used to improve sample quality in generative models.
41. How can generative models be used for data privacy protection?
Generative models can be used to generate synthetic data that preserves the statistical properties of the original data while protecting sensitive information, allowing for privacy-preserving data sharing or analysis.
42. Explain the concept of fairness in generative modeling.
Fairness in generative modeling involves ensuring that the generated outputs do not exhibit biases or discrimination against specific groups or individuals, particularly in sensitive domains such as healthcare or finance.
43. What are some techniques for measuring fairness in generative models?
Fairness metrics such as demographic parity, equalized odds, or disparate impact can be used to measure fairness in generative models.
44. How do you handle domain shift in generative modeling tasks?
Techniques such as domain adaptation, data augmentation, or adversarial training can be used to mitigate domain shift in generative modeling tasks.
45. Explain the concept of semi-supervised learning in generative modeling.
Semi-supervised learning involves training models using a combination of labeled and unlabeled data, often through techniques such as self-training, consistency regularization, or pseudo-labeling.
46. What are some challenges in training generative models with limited data?
Challenges include overfitting, lack of diversity in generated samples, and difficulty in capturing complex data distributions with limited training data.
47. Explain the concept of lifelong learning in the context of generative models.
Lifelong learning involves continuously learning from new data and tasks over time, allowing generative models to adapt and improve performance on evolving tasks or datasets.
48. What are some techniques for incorporating domain knowledge into generative models?
Techniques such as knowledge distillation, expert guidance, or incorporating auxiliary tasks can be used to incorporate domain knowledge into generative models.
49. How do you handle uncertainty estimation in generative modeling?
Techniques such as Monte Carlo dropout, ensemble methods, or Bayesian neural networks can be used to estimate uncertainty in generative models.
50. What are some emerging trends or future directions in generative AI?
Examples include few-shot learning, meta-learning, and neuro-symbolic approaches, as well as applications in fields such as drug discovery, robotics, and personalized content generation.