Artificial Intelligence

🔅 Machine Learning Foundations: Calculus 📝 Learn the basics of calculus concepts and techniques used to design and implement ML algorithms. 🌐 Author: Terezija Semenski 🔰 Level: Beginner ⏰ Duration: 1h 29m 📋 Topics: Calculus, Machine Learning 🔗 Join Machine Learning for more courses

Generating text is by no means a trivial task! LLMs are optimized to predict the probability of the next token, but how do we generate text with that? The naive approach is to use the probability vector generated by the model, choose the word with the highest probability, and autoregress. This is the greedy approach, but this tends to generate repetitive sentences that degenerate when they are too long. Another approach is to use the probabilities generated by the model and perform a sampling of the words based on those probabilities. Typically, we use a temperature parameter to adjust the level of randomness of this process. This allows to generate less repetitive and more creative sentences. But those 2 techniques have a problem. When we generate a sentence, we want to maximize the probability of the whole output sequence and not just the next token: P(Output sequence | Prompt) Fortunately, we can express this probability as a product of the probabilities to predict the next token: P(token 1, .., token N | Prompt) = P(token 1| Prompt) x ... P(token N |Prompt, token 1, ..., token N - 1) But solving this problem exactly is an NP-hard problem. So, instead, we can approximate the problem by choosing k candidate tokens at each iteration, testing them, and keeping the k sequences that maximize the probability of the whole sequence. In the end, we just choose the sequence with the highest probability. This is called the Beam search generation and can be mixed with the greedy and the multinomial approach. Another approach is the contrastive search, where we take into account additional metrics like fluency or diversity. At each iteration, we choose candidate tokens, penalize the probabilities with a similarity metric of the tokens that were previously generated, and choose the tokens that maximize the new score.

🤝Different types of machine learning

🔅 Machine Learning with Python: Logistic Regression 📝 Get an introduction to logistic regression by exploring how to build supervised machine learning models with Python. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 1h 19m 📋 Topics: Logistic Regression, Machine Learning, Python 🔗 Join Machine Learning for more courses

From automating repetitive tasks to boosting creativity, the best AI tools are essential for improving productivity in 2026 ✌️

RAG was supposed to make LLMs smarter. Ground them in facts. Give them memory. But the truth? Most RAG systems today are just fancy search engines—fetching chunks and hoping the model figures it out. That’s not intelligence. The real upgrade is Agentic RAG. Tools like Glean, Perplexity, and Harvey don’t just retrieve... they reason. They decide what to fetch, when to fetch, or whether they should fetch anything at all. This changes everything: • No blind embeddings • No random chunk dumps • Real, layered memory • APIs, search, and tools inside the reasoning loop The LLM stops guessing and starts thinking.

🔅 Introduction to Large Language Models 📝 Learn about large language models—what they are, what they can do, and how they work. 🌐 Author: Jonathan Fernandes 🔰 Level: Intermediate ⏰ Duration: 1h 17m 📋 Topics: Large Language Models 🔗 Join Artificial Intelligence and Machine Learning for more courses

🔰 Python library for finetuning Gemma 3

Includes papers on finetuning, sharding, LoRA, PEFT, multimodality, and tokenization in LLM.

pip install gemma

🌐 Documentation

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⭐️ 5 Techniques to Fine-Tune Large Language Models (LLMs) With the rise of large language models (LLMs), fine-tuning for specific tasks has become more important than ever. But how can we do it efficiently without compromising performance? 🤔 Here are 5 advanced techniques that can help: 1⃣ LoRA (Low-Rank Adaptation)

LoRA reduces the number of trainable parameters by adding low-rank adaptation matrices, making fine-tuning faster and more memory-efficient.

🔢 LoRA-FA (LoRA with Feature Augmentation)

This method combines LoRA with external feature augmentation, injecting task-specific features to further boost performance with minimal overhead.

🔢 Vera (Virtual Embedding Regularization Adaptation)

Vera helps regularize model embedding during fine-tuning, preventing over-fitting and improving generalization across different domains.

🔢 Delta LoRA

An extension of LoRA, this approach focuses on updating only the most significant layers, reducing computational costs while retaining fine-tuning effectiveness.

🔢 Prefix Tuning

Instead of modifying model weights, this technique learns task-specific prefix tokens that steer the model’s output, enabling efficient adaptation to new tasks.

🔅 Machine Learning Foundations: Statistics 📝 Learn how statistics can help you troubleshoot issues, optimize performance, and innovate, creating new machine learning models that are more efficient. 🌐 Author: Terezija Semenski 🔰 Level: Beginner ⏰ Duration: 1h 20m 📋 Topics: Statistical Analysis, Machine Learning 🔗 Join Artificial Intelligence and Machine Learning for more courses