👋 Open to Work
Mithilesh Nakade
Man1103
AI & ML interests
Classical Machine Learning (LinReg, LogReg, KNN, SVC) ||
Deep Machine Learning (GNNs, CNNs, RNNs/LSTM/GRU, Transformers) ||
AI system with LLMs, ViTs, VLMs, VLAs, World Models, etc.
Recent Activity
updated a collection 15 days ago
MN/Models/Audio updated a collection 15 days ago
MN/Models/Audio updated a collection 15 days ago
MN/Models/AudioOrganizations
None yet
Bio-AI Models such as protein classification, protein predictions, DNA sequencing, DNA and protein synthesis generation, etc.
Datasets for training Spatial models using deep learning including geospatial, dimensinality-increment such as 2d-to-3d Models, etc.
Datasets for training Audio models using deep learning for audio classification, speech recognition, Text-to-Audio (TTA), etc.
Timeseries models using deep learning for forecasting, financial prediction, sensors monitoring, etc.
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amazon/chronos-2
Time Series Forecasting • 0.1B • Updated • 8.05M • 571 -
NeoQuasar/Kronos-base
Time Series Forecasting • 0.1B • Updated • 798k • 324 -
Salesforce/moirai-1.0-R-small
Time Series Forecasting • 13.8M • Updated • 9.87k • 31 -
google/timesfm-2.5-200m-transformers
Time Series Forecasting • 0.2B • Updated • 26.5k • 147
Datasets for training Timeseries models using deep learning for forecasting, financial prediction, sensors monitoring, etc.
Datasets for training PhysicalAI models such as CLIP, VLMs, VLAs, World Action Models, etc. for EmbodiedAI, autonomous vehicles using deep learning.
Datasets for training Text models using deep learning for text classification, text generation, embeddings, NER, etc.
Spatial models including geospatial, dimensinality-increment such as 2d-to-3d Models, etc.
Audio models using deep learning for audio classification, speech recognition, Text-to-Audio (TTA), etc.
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openai/whisper-base
Automatic Speech Recognition • 72.6M • Updated • 4.64M • 313 -
Qwen/Qwen3-ASR-1.7B
Automatic Speech Recognition • 2B • Updated • 1.72M • • 1.23k -
ibm-granite/granite-speech-4.1-2b
Automatic Speech Recognition • 2B • Updated • 153k • 168 -
openai/whisper-large-v3
Automatic Speech Recognition • 2B • Updated • 3.52M • • 6.78k
Text models using deep learning for text classification, text generation, embeddings, NER, etc.
PhysicalAI models such as CLIP, VLMs, VLAs, World Action Models, etc. for EmbodiedAI, autonomous vehicles using deep learning.
Bio-AI Models such as protein classification, protein predictions, DNA sequencing, DNA and protein synthesis generation, etc.
Datasets for training Spatial models using deep learning including geospatial, dimensinality-increment such as 2d-to-3d Models, etc.
Spatial models including geospatial, dimensinality-increment such as 2d-to-3d Models, etc.
Datasets for training Audio models using deep learning for audio classification, speech recognition, Text-to-Audio (TTA), etc.
Audio models using deep learning for audio classification, speech recognition, Text-to-Audio (TTA), etc.
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openai/whisper-base
Automatic Speech Recognition • 72.6M • Updated • 4.64M • 313 -
Qwen/Qwen3-ASR-1.7B
Automatic Speech Recognition • 2B • Updated • 1.72M • • 1.23k -
ibm-granite/granite-speech-4.1-2b
Automatic Speech Recognition • 2B • Updated • 153k • 168 -
openai/whisper-large-v3
Automatic Speech Recognition • 2B • Updated • 3.52M • • 6.78k
Timeseries models using deep learning for forecasting, financial prediction, sensors monitoring, etc.
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amazon/chronos-2
Time Series Forecasting • 0.1B • Updated • 8.05M • 571 -
NeoQuasar/Kronos-base
Time Series Forecasting • 0.1B • Updated • 798k • 324 -
Salesforce/moirai-1.0-R-small
Time Series Forecasting • 13.8M • Updated • 9.87k • 31 -
google/timesfm-2.5-200m-transformers
Time Series Forecasting • 0.2B • Updated • 26.5k • 147
Datasets for training Timeseries models using deep learning for forecasting, financial prediction, sensors monitoring, etc.
Datasets for training PhysicalAI models such as CLIP, VLMs, VLAs, World Action Models, etc. for EmbodiedAI, autonomous vehicles using deep learning.
Text models using deep learning for text classification, text generation, embeddings, NER, etc.
Datasets for training Text models using deep learning for text classification, text generation, embeddings, NER, etc.
PhysicalAI models such as CLIP, VLMs, VLAs, World Action Models, etc. for EmbodiedAI, autonomous vehicles using deep learning.