Replying to @Rajiv Shah long version of deep reinforcement learning video from Week 4
6:12
Replying to @Rajiv Shah long version of deep reinforcement learning video from Week 4
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TikTokrajistics
Contrastive learning is common for folks working in NLP and images. This was new to me, so wanted to share the intuition a bit more widely. This is an introduction, there are so many contrastive loss functions for different scenarios.
1:18
Contrastive learning is common for folks working in NLP and images. This was new to me, so wanted to share the intuition a bit more widely. This is an introduction, there are so many contrastive loss functions for different scenarios.
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TikTokrajistics
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4:48
Deep dive on how to improve large language models. I provide an introduction to zero-shot and few-shot learning methods. I also discuss the role of in-context learning and emergence. For fine-tuning, the video explains instruction tuning, reinforcement learning with human feedback (rlhf), reinforcement learning with AI feedback (rlaif, and parameter efficient fine tuning (peft). I will also have a larger version of this video on my youtube, where it's easier to see the slides. #datascience #mach
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0:41
Zero shot learning #datascience #machinelearning #huggingface #nlp #naturallanguageprocessing #statistics background: @rajistics #codetok
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Should you take the time to learn Kubernetes as a data scientist? Or you already overloaded learning data science? #datascience #machinelearning #kubernetes
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1:29
Active learning uses an algorithm to help select what data to label. Ideally, using this approach, people can get comparable model results using less labeled data. #datascience #machinelearning #activelearning #datalabeling Active Learning Strategies from Neptune.ai: https://neptune.ai/blog/active-learning-strategies-tools-use-cases
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13:50
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