🎯 Key Takeaways for quick navigation: 00:07 📚 *Introduction to GPT and BERT* - GPT and BERT are both Transformer-based models. - GPT stands for Generative Pre-Trained Transformer, while BERT stands for Bidirectional Encoder Representations from Transformers. 05:26 🧠 *How BERT Works* - BERT is a stack of encoders with multiple layers and attention heads. - It is trained by masking words in sentences and predicting the masked words, making it bidirectional in nature. 10:17 🏭 *Applications of BERT* - BERT can be used in various applications by fine-tuning its pretrained model. - It's especially useful for tasks like sentiment analysis and can handle domain-specific tasks. 14:55 🧬 *Domain-Specific BERT Models* - There are domain-specific BERT models trained for specific fields like bioinformatics and finance. - These models can be used in applications within their respective domains. 25:09 📝 *Introduction to GPT* - GPT is a stack of decoder layers, where each decoder is similar to the transformer decoder but without cross-attention. - GPT is trained to predict the next word in a sequence. 29:48 🚀 *GPT's Evolution* - GPT models have evolved over time, with each version becoming larger and more powerful in terms of parameters. - GPT-4, for instance, has an enormous 175 billion parameters, making it highly capable in natural language understanding and generation. 30:28 🧠 *Introduction to GPT 4 and its size* - Introduction to GPT 4 and its undisclosed size. - Speculation on the impact of model size on performance. 34:04 🌐 *T5: Combining BERT and GPT* - T5 is a combination of BERT and GPT. - Transformation of various NLP problems into text-to-text format. - The application of T5 to a wide range of NLP tasks. 44:12 🔐 *Challenges in Aligning Language Models with User Intent* - The challenge of aligning language models with user instructions. - The importance of alignment for ethical and practical reasons. - The need to avoid harmful or offensive responses. 49:30 🎯 *Steps for Alignment and Reinforcement Learning* - Overview of the three steps for alignment: Supervised, Fine-Tuning, and Reinforcement Learning. - Introduction to reinforcement learning from human feedback. - The importance of understanding reinforcement learning for alignment. Made with HARPA AI
Thanks in advanced.so that can replace actual data to get required predication i think it is very easy for a person who has knowledge of CNN/NN THANKS SIR. say example, I have lid driven cavity problem I get velocities u and v of bith sizes are 2 dimensional (say 33 by 33 example) , it is time dependent so I want to use cnn to predict u for t=25 providing u for t= 10,15,20. And I will give actual u at t=25 for comparison and add statistical regressions , loss gain, training plots. Thanks sir. Please kindly help and I would be grateful and appreciative for kindness and support.
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🎯 Key Takeaways for quick navigation:
00:07 📚 *Introduction to GPT and BERT*
- GPT and BERT are both Transformer-based models.
- GPT stands for Generative Pre-Trained Transformer, while BERT stands for Bidirectional Encoder Representations from Transformers.
05:26 🧠 *How BERT Works*
- BERT is a stack of encoders with multiple layers and attention heads.
- It is trained by masking words in sentences and predicting the masked words, making it bidirectional in nature.
10:17 🏭 *Applications of BERT*
- BERT can be used in various applications by fine-tuning its pretrained model.
- It's especially useful for tasks like sentiment analysis and can handle domain-specific tasks.
14:55 🧬 *Domain-Specific BERT Models*
- There are domain-specific BERT models trained for specific fields like bioinformatics and finance.
- These models can be used in applications within their respective domains.
25:09 📝 *Introduction to GPT*
- GPT is a stack of decoder layers, where each decoder is similar to the transformer decoder but without cross-attention.
- GPT is trained to predict the next word in a sequence.
29:48 🚀 *GPT's Evolution*
- GPT models have evolved over time, with each version becoming larger and more powerful in terms of parameters.
- GPT-4, for instance, has an enormous 175 billion parameters, making it highly capable in natural language understanding and generation.
30:28 🧠 *Introduction to GPT 4 and its size*
- Introduction to GPT 4 and its undisclosed size.
- Speculation on the impact of model size on performance.
34:04 🌐 *T5: Combining BERT and GPT*
- T5 is a combination of BERT and GPT.
- Transformation of various NLP problems into text-to-text format.
- The application of T5 to a wide range of NLP tasks.
44:12 🔐 *Challenges in Aligning Language Models with User Intent*
- The challenge of aligning language models with user instructions.
- The importance of alignment for ethical and practical reasons.
- The need to avoid harmful or offensive responses.
49:30 🎯 *Steps for Alignment and Reinforcement Learning*
- Overview of the three steps for alignment: Supervised, Fine-Tuning, and Reinforcement Learning.
- Introduction to reinforcement learning from human feedback.
- The importance of understanding reinforcement learning for alignment.
Made with HARPA AI
Thanks in advanced.so that can replace actual data to get required predication i think it is very easy for a person who has knowledge of CNN/NN THANKS SIR. say example, I have lid driven cavity problem I get velocities u and v of bith sizes are 2 dimensional (say 33 by 33 example) , it is time dependent so I want to use cnn to predict u for t=25 providing u for t= 10,15,20. And I will give actual u at t=25 for comparison and add statistical regressions , loss gain, training plots. Thanks sir. Please kindly help and I would be grateful and appreciative for kindness and support.
pathetic
Kindly help me sir i am failing miserably
It is simple sir please accept my humble request 🙏
Learn some respect.