The Mind at Work: Guido van #Rossum on how #Python makes thinking in code easier
A conversation with the creator of the world’s most popular programming language on removing brain friction for better work
“You primarily write your code to communicate with other coders, and, to a lesser extent, to impose your will on the computer.”
—Guido van Rossum
Link
🔭 @DeepGravity
A conversation with the creator of the world’s most popular programming language on removing brain friction for better work
“You primarily write your code to communicate with other coders, and, to a lesser extent, to impose your will on the computer.”
—Guido van Rossum
Link
🔭 @DeepGravity
Dropbox
The Mind at Work: Guido van Rossum on how Python makes thinking in code easier
A conversation with the creator of the world’s most popular programming language on removing brain friction for better work.
Free #AI #Resources
Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)
Link
🔭 @DeepGravity
Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)
Link
🔭 @DeepGravity
MarkTechPost
Free AI/ Data Science Resources
Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, R Programming Resources.
Code Faster in #Python with Intelligent Snippets
#Kite is a plugin for your IDE that uses machine learning to give you useful code completions for Python. Start coding faster today.
Kite
🔭 @DeepGravity
#Kite is a plugin for your IDE that uses machine learning to give you useful code completions for Python. Start coding faster today.
Kite
🔭 @DeepGravity
Code Faster with Kite
Kite is saying farewell
From 2014 to 2021, Kite was a startup using AI to help developers write code. We have stopped working on Kite, and are no longer supporting the Kite software. Thank you to everyone who used our product, and thank you to our team members and investors who…
The Pros and Cons of Using #JavaScript for #MachineLearning
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
DLabs
The Pros and Cons of Using JavaScript for Machine Learning - DLabs
There’s a misconception in the world of machine learning (ML) Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. Python and Java often top the list. Python for its simplicity:…
#Autograd
Autograd can automatically differentiate native #Python and #Numpy code.
#Google #JAX
JAX is Autograd and XLA, brought together for high-performance machine learning research.
🔭 @DeepGravity
Autograd can automatically differentiate native #Python and #Numpy code.
#Google #JAX
JAX is Autograd and XLA, brought together for high-performance machine learning research.
🔭 @DeepGravity
GitHub
GitHub - HIPS/autograd: Efficiently computes derivatives of NumPy code.
Efficiently computes derivatives of NumPy code. Contribute to HIPS/autograd development by creating an account on GitHub.
Image Data Augmentation for #TensorFlow 2, #Keras and #PyTorch with Albumentations in #Python
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
Curiousily
Image Data Augmentation for TensorFlow 2, Keras and PyTorch with Albumentations in Python - Adventures in Artificial Intelligence…
Learn how to augment image data for Image Classification, Object Detection, and Image Segmentation
secml: A #Python Library for Secure and Explainable #MachineLearning
We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0, and hosted at https://gitlab.com/secml/secml.
Paper
🔭 @DeepGravity
We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0, and hosted at https://gitlab.com/secml/secml.
Paper
🔭 @DeepGravity
GitLab
Secure Machine Learning / SecML · GitLab
A Python library for Secure and Explainable Machine Learning Documentation available @ https://secml.gitlab.io Follow us on Twitter @