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
✴️ @AI_Python_EN
#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #objectdetection
✴️ @AI_Python_EN
#DataScience Learning Path For Complete Beginners:
https://bit.ly/2JhcjXW
#BigData #MachineLearning #AI #DataScientists #Python
✴️ @AI_Python
https://bit.ly/2JhcjXW
#BigData #MachineLearning #AI #DataScientists #Python
✴️ @AI_Python
A Practical Introduction to #DeepLearning with #Python
http://bit.ly/2QbmApb
#MachineLearning
✴️ @AI_Python
http://bit.ly/2QbmApb
#MachineLearning
✴️ @AI_Python
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
✴️ @AI_Python
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
✴️ @AI_Python
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
✴️ @AI_Python
Full version: https://www.youtube.com/watch?v=Z6rxFNMGdn0
✴️ @AI_Python
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/
✴️ @AI_Python_EN
https://code.fb.com/ai-research/pythia/
✴️ @AI_Python_EN
Course material for STAT 479: #DeepLearning (SS 2019) course at University Wisconsin-Madison
🌎 Learn more
✴️ @AI_Python_EN
🌎 Learn more
✴️ @AI_Python_EN
#MachineLearning in Agriculture: Applications and Techniques
🌎 Machine Learning in Agriculture
✴️ @AI_Python_EN
🌎 Machine Learning in Agriculture
✴️ @AI_Python_EN
Detection Free Human Instance Segmentation using Pose2Seg and PyTorch
https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
✴️ @AI_Python_EN
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
✴️ @AI_Python_EN
https://hackernoon.com/10-free-python-programming-courses-for-beginners-to-learn-online-38312f3b9912
✴️ @AI_Python_EN
Korbit is now launching the world’s first deep learning course taught by an interactive deep learning tutor. The online course is a four-week-long introduction to #machinelearning and deep learning, featuring lectures from Mila professors Yoshua Bengio, Laurent Charlin, Audrey Durand and Aaron Courville, and includes over 100 interactive exercises (question-answering exercises, drag-and-drop exercises and mathematical problems).
The #deeplearning tutor Korbi guides students through the course with a problem-solving approach and offers them different exercises, hints and visual diagrams based on their individual level of understanding and unique learning profile. The course is free and available for everyone at: www.korbit.ai/machinelearning.
✴️ @AI_Python_EN
The #deeplearning tutor Korbi guides students through the course with a problem-solving approach and offers them different exercises, hints and visual diagrams based on their individual level of understanding and unique learning profile. The course is free and available for everyone at: www.korbit.ai/machinelearning.
✴️ @AI_Python_EN
🔸Inside TensorFlow: Summaries and TensorBoard
🌐 https://www.youtube.com/watch?v=OI4cskHUslQ
✴️ @AI_Python_EN
🌐 https://www.youtube.com/watch?v=OI4cskHUslQ
✴️ @AI_Python_EN
YouTube
Inside TensorFlow: Summaries and TensorBoard
Take an inside look into the TensorFlow team’s own internal training sessions--technical deep dives into TensorFlow by the very people who are building it!
This week we take a look into TensorBoard with Nick Felt, an Engineer on the TensorFlow team. Learn…
This week we take a look into TensorBoard with Nick Felt, an Engineer on the TensorFlow team. Learn…