🔻Data for Deep Learning
🔹Types of Data:
1. sound
2. text
3. images
4. time series
5. video
🔹Use Cases:
1. classification
2. clustering
3. predictions
🔹Data Attributes:
1. relevancy
2. proper classification
3. formatting
4. accessibility
🔹Minimum Data Requirement:
The minimums vary with the complexity of the problem, but 100,000 instances in total, across all categories, is a good place to start.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://pathmind.com/wiki/data-for-deep-learning
#deeplearning
#machinelearning
#neuralnetworks
#classification
#clustering
#data
🔹Types of Data:
1. sound
2. text
3. images
4. time series
5. video
🔹Use Cases:
1. classification
2. clustering
3. predictions
🔹Data Attributes:
1. relevancy
2. proper classification
3. formatting
4. accessibility
🔹Minimum Data Requirement:
The minimums vary with the complexity of the problem, but 100,000 instances in total, across all categories, is a good place to start.
———————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://pathmind.com/wiki/data-for-deep-learning
#deeplearning
#machinelearning
#neuralnetworks
#classification
#clustering
#data
Data Mining Methods for Recommender Systems.pdf
481 KB
📕 Data Mining Methods for Recommender Systems
✒️ by Xavier Amatriain
—————
📌Via: @cedeeplearning
#datamining #recommendersystems
#clustering #classification #regression
#machinelearning #datascience
✒️ by Xavier Amatriain
—————
📌Via: @cedeeplearning
#datamining #recommendersystems
#clustering #classification #regression
#machinelearning #datascience