Skip to content
Skip to footer
AI News
All Categories
About
Contacts
Search
Search
AI News
All Categories
About
Contacts
Search
Search
Search
Search
Accuracy and Loss
Activation Function
AI Chips for Training and Inference
Artifacts
Artificial General Intelligence (AGI)
AUC (Area under the ROC Curve)
Automated Machine Learning (AutoML)
CI/CD for Machine Learning
Comparison of ML Frameworks
Confusion Matrix
Containers
Convergence
Convolutional Neural Network (CNN)
Data Science vs Machine Learning vs Deep Learning
Datasets and Machine Learning
Distributed Training (TensorFlow, MPI, & Horovod)
Epochs, Batch Size, & Iterations
ETL
Features, Feature Engineering, & Feature Stores
Generative Adversarial Network (GAN)
Gradient Boosting
Gradient Descent
Hyperparameter Optimization
Interpretability
Jupyter Notebooks
Kubernetes
Linear Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Machine Learning Models Explained
Machine Learning Operations (MLOps)
Managing Machine Learning Models
Metrics in Machine Learning
ML Showcase
MNIST
Model Deployment (Inference)
Model Drift & Decay
Model Training
Overfitting vs Underfitting
Random Forest
Recurrent Neural Network (RNN)
Reproducibility in Machine Learning
REST and gRPC
Serverless ML: FaaS and Lambda
Structured vs Unstructured Data
Supervised, Unsupervised, & Reinforcement Learning
Synthetic Data
Tensor Processing Unit (TPU)
TensorBoard
Transfer Learning
Weights and Biases
Menu
Accuracy and Loss
Activation Function
AI Chips for Training and Inference
Artifacts
Artificial General Intelligence (AGI)
AUC (Area under the ROC Curve)
Automated Machine Learning (AutoML)
CI/CD for Machine Learning
Comparison of ML Frameworks
Confusion Matrix
Containers
Convergence
Convolutional Neural Network (CNN)
Data Science vs Machine Learning vs Deep Learning
Datasets and Machine Learning
Distributed Training (TensorFlow, MPI, & Horovod)
Epochs, Batch Size, & Iterations
ETL
Features, Feature Engineering, & Feature Stores
Generative Adversarial Network (GAN)
Gradient Boosting
Gradient Descent
Hyperparameter Optimization
Interpretability
Jupyter Notebooks
Kubernetes
Linear Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Machine Learning Models Explained
Machine Learning Operations (MLOps)
Managing Machine Learning Models
Metrics in Machine Learning
ML Showcase
MNIST
Model Deployment (Inference)
Model Drift & Decay
Model Training
Overfitting vs Underfitting
Random Forest
Recurrent Neural Network (RNN)
Reproducibility in Machine Learning
REST and gRPC
Serverless ML: FaaS and Lambda
Structured vs Unstructured Data
Supervised, Unsupervised, & Reinforcement Learning
Synthetic Data
Tensor Processing Unit (TPU)
TensorBoard
Transfer Learning
Weights and Biases
Close
AI News
All Categories
About
Contacts
AI Shorts
AI Tool
Applications
Artificial Intelligence
Editors Pick
Python
PyTorch
Staff
Tech News
Technology
Uncategorized
Introducing IPEX-LLM: A PyTorch Library for Executing LLMs on Intel CPU and GPU
April 8, 2024
0
Comments
Favorite
Leave a comment
Cancel reply
0.0
/
5
Name
E-mail
Save my name, email, and website in this browser for the next time I comment.
Comment
I agree that my submitted data is being collected and stored.
You May Also Like
IoT
,
Uncategorized
Understanding Integrated Payables and Its Benefits for Your Business
AI Paper Summary
,
AI Shorts
,
Applications
,
Artificial Intelligence
,
Editors Pick
,
Language Model
,
Large Language Model
,
Machine learning
,
Staff
,
Tech News
,
Technology
,
Uncategorized
Researchers at DeepSeek AI have suggested implementing Expert-Specialized Fine-Tuning (ESFT) as a way to cut down memory usage by as much as 90% and reduce processing time by up to 30%.
Facebook
Instagram
+60 12-462 2768
hello@goacademyai.com
