LLM Evaluation Tools - DeepEval transforms LLM evaluations into a two-line test suite [1] - DeepEval helps identify the best models, prompts, and architecture for AI workflows, including MCPs (Multi-Choice Preference) [1] - DeepEval is 100% open-source with 11 thousand stars [1] Framework Compatibility - DeepEval works with all frameworks like LlamaIndex, CrewAI, etc [1] Community Engagement - The author encourages readers to reshare the information [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1]

GitHub repo: https://t.co/LfM6AdsO74(Don't forget to star 🌟) ...

Pytest for LLM Apps is finally here!DeepEval turns LLM evals into a two-line test suite to help you identify the best models, prompts, and architecture for AI workflows (including MCPs).Works with all frameworks like LlamaIndex, CrewAI, etc.100% open-source with 11k stars! https://t.co/Xayu1aFGFV ...

RT Avi Chawla (@_avichawla)Researchers from AssemblyAI built a state-of-the-art model that:- transcribes speech across 99 languages.- works even if the audio has many speakers.- outperforms Deepgram and OpenAI models.And much more.(2-step setup below) https://t.co/7eg0zpE4pM ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.AAvi Chawla (@_avichawla):Researchers from AssemblyAI built a state-of-the-art model that:- transcribes speech across 99 languages.- works even if the audio has many speakers.- outperforms Deepgram and OpenAI models.And much more.(2-step setup below) https://t.co/7eg0zpE4pM ...

Try it here with zero setup: https://t.co/ZIfq4ugtkYAssemblyAI can:- Process 1 hr of speech in ~35s- Provide industry-leading accuracy of 93.3%- Support diarization to detect multiple speakers- Detect speech in 99 languagesThanks to AssemblyAI for working with me today! ...

Researchers from AssemblyAI built a state-of-the-art model that:- transcribes speech across 99 languages.- works even if the audio has many speakers.- outperforms Deepgram and OpenAI models.And much more.(2-step setup below) https://t.co/7eg0zpE4pM ...

Dropout Mechanism - During training, the average neuron input is significantly lower compared to inference, potentially causing numerical instability due to activation scale misalignment [1] - Dropout addresses this by multiplying inputs during training by a factor of 1/(1-p), where 'p' is the dropout rate [2] - For example, with a dropout rate of 50%, an input of 50 is scaled to 100 (50 / (1 - 0.5) = 100) [2] - This scaling ensures coherence between training and inference stages for the neural network [2] Training vs Inference - Consider a layer with 100 neurons, each with an activation value of 1, and a weight of 1 from each neuron to neuron 'A' in the next layer [2] - With a 50% dropout rate, approximately 50 neurons are active during training [2] - During inference, all 100 neurons are active since Dropout is not used [2]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. ...

You can also verify this:- Create a dropout layer in PyTorch- Compute the dropout on a tensor- Now set the dropout layer to eval mode- Compute dropout on the same tensor againCheck this 👇 https://t.co/p5u7Need4G ...