Microsoft launched the best course on Generative AI!

The Free 21 lesson course is available on #Github and will teach you everything you need to know to start building #GenerativeAI applications.

Enroll: https://github.com/microsoft/generative-ai-for-beginners

https://github.com/microsoft/generative-ai-for-beginners 🩷

Stanford Lecture Notes on RNNs and LSTMs:

https://web.stanford.edu/~jurafsky/slp3/8.pdf

https://t.iss.one/DataScienceM 🔗

LLM, SLM, FLM, and MoE: Understanding which architecture fits your specific use case has its advantage.

Modern AI development requires strategic thinking about architecture selection from day one. Each of these four approaches represents a fundamentally different trade-off between computational resources, specialized performance, and deployment flexibility.

The stakes are higher than most people realize, choosing the wrong architecture doesn't just impact performance metrics, it can derail entire projects, waste months of development cycles, and consume budgets that could have delivered significantly better results with the right initial architectural decision.

🔹 1. LLMs are strong at complex reasoning tasks : Their extensive pretraining on various datasets produces flexible models that handle intricate, multi-domain problems. These problems require a broad understanding and deep contextual insight.

🔹 2. SLMs focus on efficiency instead of breadth : They are designed with smaller datasets and optimized tokenization, making them suitable for mobile applications, edge computing, and real-time systems where speed and resource limits matter.

🔹 3. FLMs deliver domain expertise through specialization : By fine-tuning base models with domain-specific data and task-specific prompts, they consistently outperform general models in specialized fields like medical diagnosis, legal analysis, and technical support.

🔹 4. MoE architectures allow for smarter scaling : Their gating logic activates only the relevant expert layers based on the context. This feature makes them a great choice for multi-domain platforms and enterprise applications needing efficient scaling while keeping performance high.

The essential factor is aligning architecture capabilities with your actual needs: performance requirements, latency limits, deployment environment, and cost factors.

Success comes from picking the right tool for the task, not necessarily the most impressive one on paper.

https://t.iss.one/DataScienceM 🖕

Best Practice for R :: Cheat Sheet

More: https://github.com/wurli/r-best-practice

#rstats #stats #datascience

https://t.iss.one/DataScienceM 💙

Statistical Signal Processing:

https://ee.stanford.edu/~gray/sp.pdf

https://t.iss.one/DataScienceM 📌

Autoencoder by Hand ✍️

The autoencoder model is the basis for training foundational models from a ton of data. We are talking about tens of billions of training examples, like a good portion of the Internet.

With that much data, it is not economically feasible to hire humans to label all of those data to tell a model what its targets are. Thus, people came up with many clever ideas to derive training targets from the training examples themselves [auto]matically.

The most straightforward idea is to just use the training data itself as the targets. This hands-on exercise demonstrates this idea.

more: https://www.byhand.ai/p/13-can-you-calculate-an-autoencoder

https://t.iss.one/DataScienceM 😱