How To Define A Convolutional Layer In PyTorch
http://bit.ly/2JFFlxI
#AI #DeepLearning #MachineLearning
#DataScience
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http://bit.ly/2JFFlxI
#AI #DeepLearning #MachineLearning
#DataScience
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📍list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning
🔗 Repo github: https://github.com/hurcy/awesome-ehr-deeplearning
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🔗 Repo github: https://github.com/hurcy/awesome-ehr-deeplearning
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A_Step_by_Step_Introduction_to_the.pdf
1.4 MB
A Step-by-Step Introduction to Object Detection Algorithms (10 pages) - Pulkit Sharma
#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #objectdetection
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#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #objectdetection
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#DataScience Learning Path For Complete Beginners:
https://bit.ly/2JhcjXW
#BigData #MachineLearning #AI #DataScientists #Python
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https://bit.ly/2JhcjXW
#BigData #MachineLearning #AI #DataScientists #Python
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A Practical Introduction to #DeepLearning with #Python
http://bit.ly/2QbmApb
#MachineLearning
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http://bit.ly/2QbmApb
#MachineLearning
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There are now many methods we can use when our dependent variable is not continuous. SVM, XGBoost and Random Forests are some popular ones.
There are also "traditional" methods, such as Logistic Regression. These usually scale well and, when used properly, are competitive in terms of predictive accuracy.
They are probabilistic models, which gives them additional flexibility. They also are often easier to interpret, critical when the goal is explanation, not just prediction.
They can be more work, however, and are probably easier to misuse than newer methods such as Random Forests. Here are some excellent books on these methods that may be of interest:
- Categorical Data Analysis (Agresti)
- Analyzing Categorical Data (Simonoff)
- Regression Models for Categorical Dependent Variables (Long and Freese)
- Generalized Linear Models and Extensions (Hardin and Hilbe)
- Regression Modeling Strategies (Harrell)
- Applied Logistic Regression (Hosmer and Lemeshow)
- Logistic Regression Models (Hilbe)
- Analysis of Ordinal Categorical Data (Agresti)
- Applied Ordinal Logistic Regression (Liu)
- Modeling Count Data (Hilbe)
- Negative Binomial Regression (Hilbe)
- Handbook of Survival Analysis (Klein et al.)
- Survival Analysis: A Self-Learning Text (Kleinbaum and Klein)
#statistics #book #Machinelearning
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There are also "traditional" methods, such as Logistic Regression. These usually scale well and, when used properly, are competitive in terms of predictive accuracy.
They are probabilistic models, which gives them additional flexibility. They also are often easier to interpret, critical when the goal is explanation, not just prediction.
They can be more work, however, and are probably easier to misuse than newer methods such as Random Forests. Here are some excellent books on these methods that may be of interest:
- Categorical Data Analysis (Agresti)
- Analyzing Categorical Data (Simonoff)
- Regression Models for Categorical Dependent Variables (Long and Freese)
- Generalized Linear Models and Extensions (Hardin and Hilbe)
- Regression Modeling Strategies (Harrell)
- Applied Logistic Regression (Hosmer and Lemeshow)
- Logistic Regression Models (Hilbe)
- Analysis of Ordinal Categorical Data (Agresti)
- Applied Ordinal Logistic Regression (Liu)
- Modeling Count Data (Hilbe)
- Negative Binomial Regression (Hilbe)
- Handbook of Survival Analysis (Klein et al.)
- Survival Analysis: A Self-Learning Text (Kleinbaum and Klein)
#statistics #book #Machinelearning
✴️ @AI_Python
New #ArtificialIntelligence Sees Like a Human, Bringing Us Closer to Skynet
Read the research: https://lnkd.in/dU9W3D4
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Read the research: https://lnkd.in/dU9W3D4
✴️ @AI_Python
Can #neuralnetworks be made to reason?" Conversation with Ian Goodfellow
Full version: https://www.youtube.com/watch?v=Z6rxFNMGdn0
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Full version: https://www.youtube.com/watch?v=Z6rxFNMGdn0
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We are open-sourcing Pythia, a #deeplearning platform to support multitasking for vision and language tasks. With Pythia, researchers can more easily build, reproduce, and benchmark AI models.
https://code.fb.com/ai-research/pythia/
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https://code.fb.com/ai-research/pythia/
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Course material for STAT 479: #DeepLearning (SS 2019) course at University Wisconsin-Madison
🌎 Learn more
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🌎 Learn more
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#MachineLearning in Agriculture: Applications and Techniques
🌎 Machine Learning in Agriculture
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🌎 Machine Learning in Agriculture
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Detection Free Human Instance Segmentation using Pose2Seg and PyTorch
https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
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https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
✴️ @AI_Python_EN
10 Free Python Programming Courses For Beginners
https://hackernoon.com/10-free-python-programming-courses-for-beginners-to-learn-online-38312f3b9912
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https://hackernoon.com/10-free-python-programming-courses-for-beginners-to-learn-online-38312f3b9912
✴️ @AI_Python_EN