Do not try, just do ...Ā ā¢Ā 1y
Random Thought : I was wondering, why ChatGPT weren't build on the Increment Learning modle.. Because I might destroy it's algo.... Let me expain.. In the world of machine learning, training models can be approached in two main ways, Batch Learning and Incremental Learning. Offline training involves training the model on a fixed dataset all at once, typically split into training, and test sets. After the initial training phase, the model is used to make predictions on new data without further updates. On the other hand, online training continuously updates the model as new data comes in, learning incrementally from each new data point or batch. This allows the model to adapt to new patterns and data changes over time. ___________________________________ If later approach were adopted, someone easily manipulated the algo by feeding the wrong data... Like 2+2=6 not 4.... ___<<<<__<<<_____<<<<__________ This in my thoughts, whats yours....?? #MachineLearning #DataScience
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"Just fine-tuned LLaMA 3.2 using Apple's MLX framework and it was a breeze! The speed and simplicity were unmatched. Here's the LoRA command I used to kick off training: ``` python lora.py \ --train \ --model 'mistralai/Mistral-7B-Instruct-v0.2' \ -
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Overfitting, underfitting, and fitting ā these aren't just technical terms, but critical checkpoints in every machine learning workflow. Understanding these concepts is key to evaluating model behavior, improving generalization, and building solutio
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One of our recent achievements showcases how optimizing code and parallelizing processes can drastically improve machine learning model training times. The Challenge: Long Training Times Our model training process was initially taking 8 hoursāslow
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Revolutionizing AI with Inference-Time Scaling: OpenAI's o1 Model" Inference-time Scaling: Focuses on improving performance during inference (when the model is used) rather than just training. Reasoning through Search: The o1 model enhances reasonin
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