Leveling Up: A Journey Through the Fundamentals of Generative AI - Season 1 Recap
Published on 22/12/2024
10 min read
In category
GenAI
I wanted to share a recap of something I’ve been working on and learning from recently, and I think others could find value in it as well. It’s a podcast series called "Gen AI Level Up," and this season, we've been on a deep dive into the foundational concepts of Generative AI. It's been an incredible journey, and I wanted to share the highlights in case anyone else is interested in a structured way to get into the field.
The Purpose of This Season
The goal of this first season was simple but ambitious: to demystify Generative AI for everyone, whether you're just starting out or looking to deepen your understanding. We wanted to provide a clear path, starting from the very basics and gradually building to more complex topics. Think of it as climbing a ladder—each rung building a solid base for the next step up. And instead of dry lectures, we wanted to provide real world examples and make the concepts accessible.
The 10-Level Learning Path
Here's a breakdown of each level, complete with resources:
Level 1: Laying the Groundwork - Neural Networks and Deep Learning
What we covered: This is where it all began! We explored the fundamentals of neural networks, understanding how these brain-inspired models function, and key elements like activation functions, backpropagation, and optimization. We also looked at the role of deep learning in capturing complex data representations.
Key Topics: Artificial Neural Networks (ANNs), Deep Learning Fundamentals, Training Neural Networks, Regularization Techniques.
Episodes:
- Deep Learning Fundamentals - Level 1
- Demystifying ANNs: The Brain-Inspired Marvel of AI - Level 1
- Teaching Machines to Learn: Inside the Training of Neural Networks - Level 1
Resources:
- Deep Learning Book: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618
- Deep Learning Specialization: https://www.deeplearning.ai/courses/deep-learning-specialization/
- Neural Networks and Deep Learning online book: http://neuralnetworksanddeeplearning.com/index.html
Level 2: Introduction to Generative Models
What we covered: We transitioned into the world of generative models, discussing the difference between generative and discriminative models. We explored how models learn the probability distribution of data, and looked at explicit vs. implicit density models.
Key Topics: Generative vs. Discriminative Models, Probabilistic Modeling, Types of Generative Models, Evaluation Metrics.
Episodes:
Resources:
- Deep Generative Modeling Paper: https://arxiv.org/abs/2103.05180
- Deep Generative Models course: https://online.stanford.edu/courses/xcs236-deep-generative-models
- Generative Models Article: https://www.geeksforgeeks.org/exploring-generative-models-applications-examples-and-key-concepts/
Level 3: Variational Autoencoders (VAEs)
What we covered: We explored how Variational Autoencoders use probability to make autoencoders generative. We discussed mathematical foundations and how the reparameterization trick works.
Key Topics: Autoencoders Recap, Probabilistic Interpretation, Latent Variable Models, ELBO, Reparameterization Trick.
Episodes:
Resources:
- Introduction to VAEs Paper: https://arxiv.org/abs/1906.02691
- VAE Tutorial: https://www.datacamp.com/tutorial/variational-autoencoders
- PyTorch VAE Tutorial: https://pyimagesearch.com/2023/10/02/a-deep-dive-into-variational-autoencoders-with-pytorch/?utm_source=chatgpt.com
Level 4: Generative Adversarial Networks (GANs)
What we covered: The magic of GANs! We looked at how these dual network systems work, and how they compete against each other to generate increasingly realistic content. We explored how both the generator and discriminator are trained, and issues with training.
Key Topics: Architecture, Adversarial Training, Minimax Game Theory, Training Challenges, Variants of GANs.
Episodes:
Resources:
- GANs Paper: https://arxiv.org/abs/1701.00160
- GANs Tutorial: https://www.analyticsvidhya.com/blog/2021/10/an-end-to-end-introduction-to-generative-adversarial-networksgans/
- Tensorflow DCGAN Tutorial: https://www.tensorflow.org/tutorials/generative/dcgan
Level 5: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
What we covered: We tackled sequence modeling, exploring how RNNs, and its variants are able to understand and process sequential data like text and speech. We dove into encoder-decoder architectures.
Key Topics: Introduction to Sequence Data, Architecture, Training Challenges, Advanced RNN Variants, Bidirectional RNNs, Sequence-to-Sequence Models
Episodes:
Resources:
- LSTM Blog Post: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
- Sequence Models Course: https://www.coursera.org/learn/nlp-sequence-models
Level 6: Attention Mechanisms and the Transformer Architecture
What we covered: We discussed the limitations of RNNs, and looked at the power of attention mechanisms. We discussed all the key components and advantages of the Transformer model, which led to breakthroughs in natural language processing.
Key Topics: Limitations of RNNs, Types of Attention, Self-Attention Mechanism, Multi-Head Attention, Positional Encoding, Advantages of Transformers.
Episodes:
Resources:
- The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
- How Transformers Work: https://www.datacamp.com/tutorial/how-transformers-work
Level 7: Advanced Transformer Architectures and Pre-trained Language Models
What we covered: Here, we discussed the most impactful Transformer models: BERT, GPT, and T5, and also discussed how these systems can be pre-trained and fine-tuned.
Key Topics: Evolution of Transformer Architectures, Pre-training and Fine-tuning Paradigm, Transfer Learning in NLP, Applications of Pre-trained Models
Episodes:
Resources:
- BERT Paper: https://arxiv.org/abs/1810.04805
- GPT-3 Paper: https://arxiv.org/abs/2005.14165
- Fine-Tuning BERT Tutorial: https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b
Level 8: Diffusion Models and Score-Based Generative Modeling
What we covered: We went deep into diffusion models, understanding how they use noise to generate high quality content, and discussed the role of Stochastic Differential Equations.
Key Topics: Introduction to Diffusion Models, Score-Based Generative Modeling, Stochastic Differential Equations (SDEs) in Generative Modeling, Applications of Diffusion Models
Episodes:
Resources:
- Diffusion Models Paper: https://arxiv.org/abs/2011.13456
- Diffusion Models Paper: https://arxiv.org/abs/2404.07771
- Understanding Diffusion Models Article: https://www.unite.ai/understanding-diffusion-models-a-deep-dive-into-generative-ai/
- MIT Open Courseware on Diffusion Models: https://ocw.mit.edu/courses/res-9-008-brain-and-cognitive-sciences-computational-tutorials/pages/diffusion-and-score-based-generative-models/
Level 9: Multimodal Generative AI Models
*What we covered: We discussed systems capable of handling multiple modalities like text, images, and audio. We looked at how cross-modal attention is applied, and some of the challenges with multimodality.*
Key Topics: Introduction to Multimodal AI, Architectures for Multimodal Generation, Training Strategies, Applications of Multimodal Generative Models, Challenges and Future Directions
Episodes:
Resources:
- Multimodal AI Article: https://www.datacamp.com/tutorial/what-is-multimodal-ai
- Multi-Modal LLM Paper: https://arxiv.org/abs/2409.14993
- Multimodal Generative AI Course: https://www.coursera.org/learn/codio-multimodal-generative-ai-vision-speech-and-assistants?utm_source=chatgpt.com
Level 10: Ethical Considerations and Future Trends in Generative AI
What we covered: We ended the season with the crucial ethical implications of Generative AI, and some of the emerging trends in the field. This served to summarize all the different concepts we looked at previously.
Key Topics: Ethical Implications of Generative AI, Frameworks and Guidelines for Ethical AI, Future Trends in Generative AI, Societal Impact and Workforce Implications
Episodes:
Resources:
- Mapping the Ethics of Generative AI Paper: https://arxiv.org/abs/2402.08323
- Frontier AI Ethics Paper: https://www.mdpi.com/2227-9709/11/3/58
- Science in the Era of ChatGPT Paper: https://arxiv.org/abs/2305.15299
Key Takeaways from This Journey
This season has really shown me that Generative AI isn't some magical black box. It's a field with deep roots in math and computer science, but one that is constantly evolving with new models and applications. Learning Generative AI means looking both at the underlying mechanics of neural networks and also the cutting edge advancements and ethical considerations that go into creating these systems.
Final Thoughts
If you’re looking to get a handle on Gen AI and want a structured place to start, I highly recommend checking out the Gen AI Level Up podcast. It's been an incredible experience, and I think it can help you “level up” your knowledge too. You can find all the episodes on Spotify (or your podcast app of choice). You can also find all the episodes in YouTube as well!
Let me know what you think! I'm always open to discussing AI, so feel free to drop a comment or connect with me.
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