Brandon Rohrer is a data scientist at Facebook. He's very knowledgeable in Machine Learning and knows how to explain complex concepts in an easy to understand manner.
Here comes his free course on #deeplearning #neuralnetwork #DeepNeuralNetworks.

How Deep Neural Networks Work

https://end-to-end-machine-learning.teachable.com/p/how-deep-neural-networks-work/


✴️ @AI_Python_EN

Google PhD Fellowship Program

Google PhD Fellowships directly support graduate students as they pursue their PhD, as well as connect them to a Google Research Mentor.

https://ai.google/research/outreach/phd-fellowship/

✴️ @AI_Python_EN

Python - Python NLP Libraries
TextBlob - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of Natural Language Toolkit (NLTK) and Pattern, and plays nicely with bot.
spaCy - Industrial strength NLP with Python and Cython
textacy - Higher level NLP built on spaCy
gensim - Python library to conduct unsupervised semantic modelling from plain text
scattertext - Python library to produce d3 visualizations of how language differs between corpora
AllenNLP - An NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.
PyTorch-NLP - NLP research toolkit designed to support rapid prototyping with better data loaders, word vector loaders, neural network layer representations, common NLP metrics such as BLEU.
Rosetta - Text processing tools and wrappers (e.g. Vowpal Wabbit).
PyNLPl - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
#naturallanguage #machinelearning #ai #language #nlp #spacy #pytorch

✴️ @AI_Python_EN

CS224N Natural Language Processing with Deep Learning 2019
YouTube playlist:
https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

http://onlinehub.stanford.edu/cs224 #NLProc

✴️ @AI_Python_EN

Yoshua: Research is like a random exploration guided by intuition. It's okay to fail, but more important is to try. At an informal event at MILA Montreal

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Are you a Data Scientists? Do you use Jupyter? Please help us understand how do you consume content and get connected with other professionals Just answer this 3 minute survey : http://bit.ly/Jupyter-survey-1 #DataScience #MachineLearning

✴️ @AI_Python_EN

Successful deployed an image classifier using #flask and #docker #100DaysOfMLCode #100daysofcode #MachineLearning #DeepLearning

https://github.com/hrishikeshmane87933/keras_webapp

✴️ @AI_Python_EN

Bill Gates: A.I. is like nuclear energy — 'both promising and dangerous' - CNBC Read more here: https://ift.tt/2uuayNC #ArtificialIntelligence #AI #DataScience #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT

✴️ @AI_Python_EN

MIT Introduction to #DeepLearning http://bit.ly/2JOvTf4 #MachineLearning #TensorFlow

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Checklist for debugging neural networks

http://bit.ly/2HSI0W5 #AI #DeepLearning #MachineLearning #DataScience

✴️ @AI_Python_EN

How to write a good machine learning tutorial.

https://bit.ly/2TFUTF6

#MachineLearning #DeepLearning

✴️ @AI_Python_EN

Machine Learning Cheat Sheet #Learning #MachineLearning

✴️ @AI_Python_EN

ArcFace: Additive Angular Margin Loss for Deep Face Recognition. The author used PyTorch 1.0 which is nice.

"We present arguably the most extensive experimental
evaluation of all the recent state-of-the-art face recognition
methods on over 10 face recognition benchmarks including
a new large-scale image database with trillion level
of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead. We release all refined training data, training codes, pre-trained models and training logs , which will help reproducet he results in this paper."

https://lnkd.in/e5Q2qP3
https://lnkd.in/ezWbVhH
✴️ @AI_Python_EN

This week's #machinelearning Q&A is on Underfitting vs Overfitting -

💡 How can you tell if your model is underfitting your data?

If your training and validation error are both relatively equal and very high, then your model is most likely underfitting your training data.

💡 How can you tell if your model is overfitting your data?

If your training error is low and your validation error is high, then your model is most likely overfitting your training data.

👉 Do you have any favorite heuristics that you use to detect under and over fitting in your models?

#datascience

✴️ @AI_Python_EN

#fun
✴️ @AI_Python_EN

Emoticons were born on this day in 1881 on the pages of Puck Magazine under the heading "Typographical Art," depicting four emotions: joy, melancholy, indifference, and astonishment https://www.brainpickings.org/2012/12/21/100-diagrams-that-changed-the-world/
✴️ @AI_Python_EN

Play with face editing SC-FEGAN directly in GoogleColab Notebook:

https://colab.research.google.com/github/zaidalyafeai/Notebooks/blob/master/SC_FEGAN.ipynb

Others: https://github.com/zaidalyafeai/Notebooks/blob/master/README.md

✴️ @AI_Python_EN

Wishes for TensorFlow/Keras 🤞

- A full merge between Keras and TF
- Make the transition from Keras to custom layers seamless
- Less announcements and more clarity on the existing API family
- An official experimental toolbox (similar to the fastai library)

✴️ @AI_Python_EN

Want to know why training on small data is the future? And more importantly, why Andrew named his daughter Nova? Learn why in Andrew’s chat with MIT Tech Review's Will Knight at #EmTechDigital 2019: http://bit.ly/2VdaGwO

✴️ @AI_Python_EN

Protecting your #DeepLearning model will be the key area to focus on from cyber attacks to your models and algorithms.

Placing these is public cloud environments may severely affect your ability to protect these models and algorithms.

You need to prepare to defend these.

What are these adversarial attacks?

1. l2-norm attacks: in these attacks the attacker aims to minimize squared error between the adversarial and original image. These typically result in a very small amount of noise added to the image.
2. l∞-norm attacks: this is perhaps the simplest class of attacks which aim to limit or minimize the amount that any pixel is perturbed in order to achieve an adversary’s goal.
3. l0-norm attacks: these attacks minimize the number of modified pixels in the image.

Below is an example of an l2-norm attack where the left is classified as jeep but the right as a minivan.

#cyberattacks #algorithms #models #deeplearning

✴️ @AI_Python_EN

You want to be a data scientist ...?
First read this excellent tutorial by https://lnkd.in/eKrDyhN: "How sure are we? Two approaches to statistical inference"
https://lnkd.in/e5JBrN4

✴️ @AI_Python_EN