🔻پنجمین دورهمی مدرسه جهانی هوش مصنوعی در رشت
🔺School of AI

📘موضوع این جلسه: عصب شناسی محاسباتی و رابطه آن با هوش مصنوعی
🗓دوشنبه 11 شهریور 1398
🕐ساعت 16:30 الی 19:00
🏢 رشت، میدان انتظام (پل رازی)، پارک علم و فناوری گیلان

*جهت ثبت نام به لینک زیر مراجعه کنید:
▪️https://evnd.co/WtokW

برای کسب اطلاعات بیشتر درباره ی این مدرسه، به کانال زیر مراجعه کنید:
🏢 @schoolofairasht

✅ @Brainandcognition_GU

What is “ML Ops”? Best Practices for DevOps for ML
https://www.itsalways10.com/what-is-ml-ops-best-practices-for-devops-for-ml/

A Shared Vision for Machine Learning in Neuroscience
https://www.jneurosci.org/content/jneuro/38/7/1601.full.pdf

Deep Neural Networks in Computational Neuroscience
https://www.biorxiv.org/content/biorxiv/early/2017/05/04/133504.full.pdf

Fast-Bert

This library will help you build and deploy BERT based models within minutes:

Fast-Bert is the deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification.

The work on FastBert is built on solid foundations provided by the excellent Hugging Face BERT PyTorch library and is inspired by fast.ai and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.

With FastBert, you will be able to:

Train (more precisely fine-tune) BERT, RoBERTa and XLNet text classification models on your custom dataset.

Tune model hyper-parameters such as epochs, learning rate, batch size, optimiser schedule and more.

Save and deploy trained model for inference (including on AWS Sagemaker).

Fast-Bert will support both multi-class and multi-label text classification for the following and in due course, it will support other NLU tasks such as Named Entity Recognition, Question Answering and Custom Corpus fine-tuning.

Blog post: https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384

Code: https://github.com/kaushaltrivedi/fast-bert

#language_model #BERT

Neuroscience and Reinforcement Learning
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf

Deep Learning and Computational Neuroscience
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6

https://www.youtube.com/watch?v=-vhYoS3751g
#neuroscience #reinforcement_learning

https://www.pnas.org/content/108/Supplement_3/15647
#neuroscience #reinforcement_learning

The Roles of Supervised Machine Learning in Systems Neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning

An Improved EM algorithm
In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM)). Our experiment with expectation maximization is performed in the following three cases: initialize randomly; initialize with result of K-means; initialize with result of K-medoids. The experiment result shows that expectation maximization algorithm depend on its initial state or parameters. And we found that EM initialized with K-medoids performed better than both the one initialized with K-means and the one initialized randomly.
https://arxiv.org/abs/1305.0626
#machine_learning #statistics

A great course about machine learning
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLEBC422EC5973B4D8
#machine_learning

A friendly introduction to machine learning
It is a great video for people who want to start learning machine learning, but they don't know a lot about it. So, it will help them to find a good insight into this field.
https://www.youtube.com/watch?v=IpGxLWOIZy4
#machine_learning

https://www.youtube.com/watch?v=JgvyzIkgxF0&t=1s
#machine_learning #reinforcement_learning

https://www.youtube.com/watch?v=Dn6b9fCIUpM
#statistics #machine_learning

https://www.youtube.com/watch?v=h9jC_V7ojqo
#quantum_physics #machine_learning #neuroscience

نمایش بصری گراف معماری شبکه های عصبی یادگیری عمیق و یادگیری ماشین

Visualizer for neural network, deep learning and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models.

Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe (.caffemodel, .prototxt), Caffe2 (predict_net.pb, predict_net.pbtxt), MXNet (.model, -symbol.json), TorchScript (.pt, .pth), NCNN (.param) and TensorFlow Lite (.tflite).

Netron has experimental support for PyTorch (.pt, .pth), Torch (.t7), CNTK (.model, .cntk), Deeplearning4j (.zip), PaddlePaddle (.zip, model), Darknet (.cfg), scikit-learn (.pkl), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta, .pbtxt).

https://github.com/lutzroeder/netron