Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in…
#machinelearning
https://bit.ly/2N5QFb3
❇️ @AI_PYTHON_EN
#machinelearning
https://bit.ly/2N5QFb3
❇️ @AI_PYTHON_EN
Mish is now even supported on YOLO v3 backend. Couldn't have been more elated with how rewarding this project has been. Link to repository -
https://github.com/digantamisra98/Mish
#neuralnetworks #mathematics #algorithms #deeplearning #machinelearning
❇️ @AI_Python_EN
https://github.com/digantamisra98/Mish
#neuralnetworks #mathematics #algorithms #deeplearning #machinelearning
❇️ @AI_Python_EN
Experience-Embedded Visual Foresight
Yen-Chen et al.:
https://arxiv.org/abs/1911.05071
Demo:
http://yenchenlin.me/evf/
#DeepLearning #MachineLearning #Robotics
❇️ @AI_Python_EN
Yen-Chen et al.:
https://arxiv.org/abs/1911.05071
Demo:
http://yenchenlin.me/evf/
#DeepLearning #MachineLearning #Robotics
❇️ @AI_Python_EN
Grid search vs randomized search?
💡 What are the pros and cons of grid search? Pros: • Grid search is great when you need to fine-tune hyperparameters over a small search space automatically. • For example, if you have 100 different datasets that you expect to be similar (e.g. solving the same problem repeatedly with different populations), you can use grid search to automatically fine-tune the hyperparameters for each model. Cons: • Grid search is computationally expensive and inefficient, often searching over parameter space that has very little chance of being useful, resulting it being extremely slow. It's especially slow if you need to search a large space since it's complexity increases exponentially as more hyperparameters are optimized.
💡 What are the pros and cons of randomized search? Pros: • Randomized search does a good job finding near-optimal hyperparameters over a very large search space relatively quickly and doesn't suffer from the same exponential scaling problem as grid search. Cons: • Randomized search does not fine-tune the results as much as grid search does since it typically does not test every possible combination of parameters.
#datascience
👉 Free training -> http://bit.ly/dsdj-webinar
❇️ @AI_Python_EN
💡 What are the pros and cons of grid search? Pros: • Grid search is great when you need to fine-tune hyperparameters over a small search space automatically. • For example, if you have 100 different datasets that you expect to be similar (e.g. solving the same problem repeatedly with different populations), you can use grid search to automatically fine-tune the hyperparameters for each model. Cons: • Grid search is computationally expensive and inefficient, often searching over parameter space that has very little chance of being useful, resulting it being extremely slow. It's especially slow if you need to search a large space since it's complexity increases exponentially as more hyperparameters are optimized.
💡 What are the pros and cons of randomized search? Pros: • Randomized search does a good job finding near-optimal hyperparameters over a very large search space relatively quickly and doesn't suffer from the same exponential scaling problem as grid search. Cons: • Randomized search does not fine-tune the results as much as grid search does since it typically does not test every possible combination of parameters.
#datascience
👉 Free training -> http://bit.ly/dsdj-webinar
❇️ @AI_Python_EN
Machine Learning w.r.t meditation routine.
Machine before meditation = underfitting
Machine after meditation = optimal fitting
Planning of meditation = overfitting
#datascience
❇️ @AI_Python_EN
Machine before meditation = underfitting
Machine after meditation = optimal fitting
Planning of meditation = overfitting
#datascience
❇️ @AI_Python_EN
Trying the online demo of GPT-2 based text generator.
https://transformer.huggingface.co/doc/gpt2-large
#deeperlearning #machinelearning
❇️ @AI_Python_EN
https://transformer.huggingface.co/doc/gpt2-large
#deeperlearning #machinelearning
❇️ @AI_Python_EN
Four keys to #machinelearning on the edge
Machine learning is hard. Moving your ML model to embedded devices can be even harder.
https://bit.ly/2KjUEh6
❇️ @AI_Python_EN
Machine learning is hard. Moving your ML model to embedded devices can be even harder.
https://bit.ly/2KjUEh6
❇️ @AI_Python_EN
25 Best Free #Dataset s for Machine Learning
https://www.edureka.co/blog/25-best-free-datasets-machine-learning/
❇️ @AI_Python_EN
https://www.edureka.co/blog/25-best-free-datasets-machine-learning/
❇️ @AI_Python_EN
MIT Technology Review:
A #NeuralNet solves the three-body problem 100 million times faster
#MachineLearning #mathematics
🔰 NeuralNet
🔰 Paper
❇️ @AI_Python_EN
A #NeuralNet solves the three-body problem 100 million times faster
#MachineLearning #mathematics
🔰 NeuralNet
🔰 Paper
❇️ @AI_Python_EN
Brown University’s Data Science Initiative (DSI) seeks applicants for a lecturer, senior lecturer, or distinguished senior lecturer. The initial appointment is for a 3-year period (renewable with potential for promotion and longer-term contracts). The position involves teaching four courses
per year and providing administrative or advising support for student programs. We seek candidates who will contribute to our overall intellectual culture. Lecturers with substantial research participation and supporting funds may be eligible for periodic course release. Please see our Interfolio posting for more details and to apply.
https://apply.interfolio.com/70943
❇️ @AI_Python_EN
per year and providing administrative or advising support for student programs. We seek candidates who will contribute to our overall intellectual culture. Lecturers with substantial research participation and supporting funds may be eligible for periodic course release. Please see our Interfolio posting for more details and to apply.
https://apply.interfolio.com/70943
❇️ @AI_Python_EN
3 Ways to Encode Categorical Variables for Deep Learning
https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/
❇️ @AI_Python_EN
https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/
❇️ @AI_Python_EN
Deep Learning in mobile and wireless networking:
https://arxiv.org/pdf/1803.04311.pdf
ML in telecom:
https://jisajournal.springeropen.com/articles/10.1186/s13174-018-0087-2
❇️ @AI_Python_EN
https://arxiv.org/pdf/1803.04311.pdf
ML in telecom:
https://jisajournal.springeropen.com/articles/10.1186/s13174-018-0087-2
❇️ @AI_Python_EN
Evolving Neural Networks
A tutorial on evolutionary algorithms
https://towardsdatascience.com/evolving-neural-networks-b24517bb3701
#NeuralNetworks #ReinforcementLearning
❇️ @AI_Python_EN
A tutorial on evolutionary algorithms
https://towardsdatascience.com/evolving-neural-networks-b24517bb3701
#NeuralNetworks #ReinforcementLearning
❇️ @AI_Python_EN
Release: CCNet is our new tool for extracting high-quality and large-scale monolingual corpora from CommonCraw in more than a hundred languages.
Paper:
https://arxiv.org/abs/1911.00359
Tool:
https://github.com/facebookresearch/cc_net
❇️ @AI_Python_EN
Paper:
https://arxiv.org/abs/1911.00359
Tool:
https://github.com/facebookresearch/cc_net
❇️ @AI_Python_EN
ai.pdf
2.1 MB
Who is winning the #AI race : China 🇨🇳, Europe🇪🇺 or US 🇺🇸?
Interesting 106 page report from Aug 2019 by "Center for data innovation"
I personally believe that innovation will come from a true borderless exchange of technology & talent in democratic and responsible societies.
#artificialintelligence #machinelearning
❇️ @AI_Python_EN
Interesting 106 page report from Aug 2019 by "Center for data innovation"
I personally believe that innovation will come from a true borderless exchange of technology & talent in democratic and responsible societies.
#artificialintelligence #machinelearning
❇️ @AI_Python_EN
Adobe Research is offering audio research internships in San Francisco and Seattle in 2020. We are looking for PhD students who are excited about pushing the state of the art in the field and having a
Please use “Audio Research Internship” as the title of your application email.
...more
❇️ @AI_Python_EN
Please use “Audio Research Internship” as the title of your application email.
...more
❇️ @AI_Python_EN
The Interactive Media Group at Microsoft Research, Redmond has several openings for research internships. For over 20 years, our interns have been conducting influential research published in top-tier venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, JASA. We are looking for motivated students in various topics including, but not limited to,
computer vision, machine learning, object detection and tracking,scene understanding,3D reconstruction, computational photography, learning from simulation, mixed/augmented reality ,unsupervised representation learning ,
multimodal learning for video understanding, physically-based sound synthesis and propagation, deep learning for vision, graphics, and acoustics
For details on our research group and projects, please see:
http://research.microsoft.com/en-us/groups/graphics/
We are located at Microsoft headquarters in Redmond, near Seattle, Washington. We strive to make our internships fun, productive, and profitable (salary, and transportation, etc.).
HOW TO APPLY:
If interested, please apply here:
https://careers.microsoft.com/us/en/job/736933/Research-Intern-Computer-Vision-Machine-Learning-Graphics-Sound-Propagation-within-IMG
❇️ @AI_Python_EN
computer vision, machine learning, object detection and tracking,scene understanding,3D reconstruction, computational photography, learning from simulation, mixed/augmented reality ,unsupervised representation learning ,
multimodal learning for video understanding, physically-based sound synthesis and propagation, deep learning for vision, graphics, and acoustics
For details on our research group and projects, please see:
http://research.microsoft.com/en-us/groups/graphics/
We are located at Microsoft headquarters in Redmond, near Seattle, Washington. We strive to make our internships fun, productive, and profitable (salary, and transportation, etc.).
HOW TO APPLY:
If interested, please apply here:
https://careers.microsoft.com/us/en/job/736933/Research-Intern-Computer-Vision-Machine-Learning-Graphics-Sound-Propagation-within-IMG
❇️ @AI_Python_EN