Recent comments in /f/deeplearning

crimson1206 t1_j8njti4 wrote

Reply to comment by danja in Physics-Informed Neural Networks by vadhavaniyafaijan

By normal NN I'm referring to a standard MLP without anything fancy going on. I.e. input -> hidden layers & activations -> output.

The universal approximation theorem isn't relevant here. Obviously a NN could fit this function given training data. This post is about lacking extrapolation capabilities/how PINNs improve extrapolation though

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tensor_searcher t1_j8mbbb9 wrote

Reply to [P] From “iron manual” to “Iron Man” — Augmenting GPT for fast editable memory to enable context aware question & answering by skeltzyboiii

A scalable vector database like Marqo as memory to be injected into GPT-like LLMs seems to be the way to go. The Bing use case shows that setting these systems up haphazardly can lead to blatantly false results. How do these systems prevent that type of performance? This would be an import problem to solve for these new tools, like LangChain.

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skeltzyboiii OP t1_j8m63ka wrote

Reply to [P] From “iron manual” to “Iron Man” — Augmenting GPT for fast editable memory to enable context aware question & answering by skeltzyboiii

TL;DR We show how an update-able and domain specific memory (via an external knowledge base) can be added to GPT to perform question and answering for products and chat agents. Some very humorous interactions arise when GPT is connected to an external knowledge base and forced to use irrelevant context in answering questions.
Article: https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmenting-gpt-with-marqo-for-fast-editable-memory-to-enable-context-aware-question-answering
Code: https://github.com/marqo-ai/marqo/tree/mainline/examples/GPT-examples

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Oceanboi t1_j8l8tst wrote

Reply to comment by Oceanboi in HOW DO I KNOW THE STEP BY STEP PROCESSES INVOLVED IN SOUND CLASSIFICATION USING CNN. Can anyone help me by pointing me towards the rght direction, be it a paid course or whatever.... I need to know how to implement it for my project work. I would really appreciate your help. Thanks by Illustrious-Force-74

I’m guessing your company won’t have the resources or data to train a CNN to convergence from scratch, so read up on some common CNNs that people use for audio transfer learning (EfficientNet has worked well for me, as did ResNet50, albeit less so). Once you can implement one pre trained model, you can implement most of them fairly easily to see which one suits your task best. Also read up on Sharan et al 2019 and 2021 as he benchmarks numerous image representations, model architectures, and network fusion techniques. While results may very, empirically it is a great starting point although I was not able to achieve his results given his model architecture. Pay less attention to the actual architecture he talks about because you’ll most likely be doing transfer learning where you’ll be importing a model and it’s weights. For preprocessing look into either MATLAB for their Auditory Modeling toolbox and if you’re using python look into librosa, torchaudio, and brian2hears for more complex filterbank models.

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Oceanboi t1_j8l880s wrote

Reply to HOW DO I KNOW THE STEP BY STEP PROCESSES INVOLVED IN SOUND CLASSIFICATION USING CNN. Can anyone help me by pointing me towards the rght direction, be it a paid course or whatever.... I need to know how to implement it for my project work. I would really appreciate your help. Thanks by Illustrious-Force-74

Oooooo buddy. You’re in for a ride. Check out some PyTorch documentation. There’s plenty related to audio classification

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ZaZaMood t1_j8kurf5 wrote

Reply to HOW DO I KNOW THE STEP BY STEP PROCESSES INVOLVED IN SOUND CLASSIFICATION USING CNN. Can anyone help me by pointing me towards the rght direction, be it a paid course or whatever.... I need to know how to implement it for my project work. I would really appreciate your help. Thanks by Illustrious-Force-74

Brooo there is sooo many. No need to pay. Just Google

CNN audio classification site:GitHub.com

Example

https://github.com/jeffprosise/Deep-Learning/blob/master/Audio%20Classification%20(CNN).ipynb

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artsybashev t1_j8i33cp wrote

Reply to comment by Appropriate_Ant_4629 in GPU comparisons: RTX 6000 ADA vs Hopper h100 by N3urAlgorithm

Yeah might be. I've only seen companies do machine learning in two ways. On is to rent a cluster of gpus and train something big for a week or two to explore something interesting. The other use pattern is to retrain a model every week with fresh data. Maybe this is the case for OP. Retraining a model each week and serving that model with some cloud platform. It makes sense to build a dedicated instance for a reoccuring tasks if you know that there is a need for it for more than a year. I guess it is also cheaper than using the upfront payment option in aws.

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redouann t1_j8hw6x8 wrote

Reply to Any of you know a local and Open Source equivalent to Eleven Labs text to speech AI ? by lordnyrox

There are several open source text-to-speech (TTS) software available that you can use as a local alternative to Eleven Labs' TTS AI. Some of the best one is: Odiofy App
it is a free tool and can be downloaded and installed on your local machine. You can then use them to convert text to speech using various voices and languages. Keep in mind that these tools may have different features, voice quality, and performance compared to TTS services like Eleven Labs.

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