🔹Using LIME to Understand a Machine Learning Model’s #Predictions
Using a record explainer mechanism like Local Interpretable #Model_Agnostic Explanations (LIME) is an important technique to filter through the predicted outcomes from any machine learning model. This technique is powerful and fair because it focuses more on the inputs and outputs from the model, rather than on the model itself.
#LIME works by making small tweaks to the input #data and then observing the impact on the output data. By #filtering through the model’s findings and delivering a more digestible explanation, humans can better gauge which predictions to trust and which will be the most valuable for the organization.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#machinelearning
#datascience
#deeplearning
#AI
Using a record explainer mechanism like Local Interpretable #Model_Agnostic Explanations (LIME) is an important technique to filter through the predicted outcomes from any machine learning model. This technique is powerful and fair because it focuses more on the inputs and outputs from the model, rather than on the model itself.
#LIME works by making small tweaks to the input #data and then observing the impact on the output data. By #filtering through the model’s findings and delivering a more digestible explanation, humans can better gauge which predictions to trust and which will be the most valuable for the organization.
———————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#machinelearning
#datascience
#deeplearning
#AI
🔹Jump-start Training for #Speech_Recognition Models in Different Languages with NVIDIA NeMo
🖊By Oleksii Kuchaiev
Transfer learning is an important machine learning technique that uses a model’s knowledge of one task to make it perform better on another. Fine-tuning is one of the techniques to perform transfer learning.
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📌Via: @cedeeplearning
https://devblogs.nvidia.com/jump-start-training-for-speech-recognition-models-with-nemo/
#deeplearning #neuralnetworks
#machinelearning #NVIDIA
#AI #datascience #math
#nemo #model #data
🖊By Oleksii Kuchaiev
Transfer learning is an important machine learning technique that uses a model’s knowledge of one task to make it perform better on another. Fine-tuning is one of the techniques to perform transfer learning.
———————
📌Via: @cedeeplearning
https://devblogs.nvidia.com/jump-start-training-for-speech-recognition-models-with-nemo/
#deeplearning #neuralnetworks
#machinelearning #NVIDIA
#AI #datascience #math
#nemo #model #data
NVIDIA Developer Blog
Jump-start Training for Speech Recognition Models in Different Languages with NVIDIA NeMo | NVIDIA Developer Blog
Transfer learning is an important machine learning technique that uses a model’s knowledge of one task to make it perform better on another. Fine-tuning is one of the techniques to perform transfer…
🔹Announcing NVIDIA NeMo: Fast Development of Speech and Language Models
🖊By Raghav Mani
🔻The inputs and outputs, coding style, and data processing layers in these models may not be compatible with each other. Worse still, you may be able to wire up these models in your code in such a way that it technically “works” but is in fact semantically wrong. A lot of time, effort, and duplicated code goes into making sure that you are reusing models safely.
🔻Build a simple ASR model to see how to use NeMo. You see how neural types provide semantic safety checks, and how the tool can scale out to multiple GPUs with minimal effort.
———————
📌Via: @cedeeplearning
https://devblogs.nvidia.com/announcing-nemo-fast-development-of-speech-and-language-models/
#deeplearning #neuralnetworks
#machinelearning #NVIDIA
#AI #datascience #math
#nemo #model #data
🖊By Raghav Mani
🔻The inputs and outputs, coding style, and data processing layers in these models may not be compatible with each other. Worse still, you may be able to wire up these models in your code in such a way that it technically “works” but is in fact semantically wrong. A lot of time, effort, and duplicated code goes into making sure that you are reusing models safely.
🔻Build a simple ASR model to see how to use NeMo. You see how neural types provide semantic safety checks, and how the tool can scale out to multiple GPUs with minimal effort.
———————
📌Via: @cedeeplearning
https://devblogs.nvidia.com/announcing-nemo-fast-development-of-speech-and-language-models/
#deeplearning #neuralnetworks
#machinelearning #NVIDIA
#AI #datascience #math
#nemo #model #data
NVIDIA Developer Blog
Announcing NVIDIA NeMo: Fast Development of Speech and Language Models | NVIDIA Developer Blog
As a researcher building state-of-the-art speech and language models, you must be able to quickly experiment with novel network architectures. This experimentation may focus on modifying existing…
⭕️ Top 6 Open Source Pre-trained Models for Text Classification you should use
1. XLNet
2. ERNIE
3. Text-to-Text Transfer Transformer (T5)
4. Binary - Partitioning Transformation (BPT)
5. Neural Attentive Bag-of-Entities Model for Text Classification (NABoE)
6. Rethinking Complex Neural Network Architectures for Document Classification
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📌Via: @cedeeplearning
https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/
#classification #machinelearning
#datascience #model #training
#deeplearning #dataset #neuralnetworks
#NLP #math #AI
1. XLNet
2. ERNIE
3. Text-to-Text Transfer Transformer (T5)
4. Binary - Partitioning Transformation (BPT)
5. Neural Attentive Bag-of-Entities Model for Text Classification (NABoE)
6. Rethinking Complex Neural Network Architectures for Document Classification
————————
📌Via: @cedeeplearning
https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/
#classification #machinelearning
#datascience #model #training
#deeplearning #dataset #neuralnetworks
#NLP #math #AI
Analytics Vidhya
Top 6 Open Source Pretrained Models for Text Classification you should use
Pretrained models and transfer learning is used for text classification. Here are the top pretrained models you shold use for text classification.
⭕️ A foolproof way to shrink deep learning models
Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.
🖋By Kim Martineau
As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models.
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📌Via: @cedeeplearning
http://news.mit.edu/2020/foolproof-way-shrink-deep-learning-models-0430
#deeplearning #AI #model
#MIT #machinelearning
#datascience #neuralnetworks
#algorithm #research
Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.
🖋By Kim Martineau
As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models.
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📌Via: @cedeeplearning
http://news.mit.edu/2020/foolproof-way-shrink-deep-learning-models-0430
#deeplearning #AI #model
#MIT #machinelearning
#datascience #neuralnetworks
#algorithm #research
MIT News
A foolproof way to shrink deep learning models
MIT researchers have proposed a technique for shrinking deep learning models that they say is simpler and produces more accurate results than state-of-the-art methods. It works by retraining the smaller, pruned model at its faster, initial learning rate.