A TensorFlow Modeling Pipeline Using TensorFlow Datasets and TensorBoard
https://www.kdnuggets.com/2020/06/tensorflow-modeling-pipeline-tensorflow-datasets-tensorboard.html
https://www.kdnuggets.com/2020/06/tensorflow-modeling-pipeline-tensorflow-datasets-tensorboard.html
KDnuggets
A TensorFlow Modeling Pipeline Using TensorFlow Datasets and TensorBoard - KDnuggets
This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task.
Lecture Notes in Deep Learning: Feedforward Networks — Part 3 | #DataScience #MachineLearning #ArtificialIntelligence #AI
https://bit.ly/2Z2GgQY
https://bit.ly/2Z2GgQY
Medium
Feedforward Networks — Part 3
The Backpropagation Algorithm
TensorFlow, Keras and deep learning, without a PhD access_tim
https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#2
https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#2
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules Mittal et al.: #ArtificialIntelligence #DeepLearning #MachineLearning
https://arxiv.org/abs/2006.16981
https://arxiv.org/abs/2006.16981
How to Write a Makefile - Automating Python Setup, Compilation, and Testing
https://stackabuse.com/how-to-write-a-makefile-automating-python-setup-compilation-and-testing/
https://stackabuse.com/how-to-write-a-makefile-automating-python-setup-compilation-and-testing/
Stack Abuse
How to Write a Makefile - Automating Python Setup, Compilation, and Testing
In this tutorial, we'll go over the basics of Makefiles - regex, target notation and bash scripting. We'll write a makefile for a Python project and then execute it with the make utility.
How to read Deep Learning research papers.
⚫ A systematic approach to reading a collection of papers to gain knowledge within a domain
⚫ How to properly read a research paper
⚫ Useful online resources that
can aid you in searching for papers and key information "50–100 papers will primarily provide you with a very good understanding of the domain."
https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3
⚫ A systematic approach to reading a collection of papers to gain knowledge within a domain
⚫ How to properly read a research paper
⚫ Useful online resources that
can aid you in searching for papers and key information "50–100 papers will primarily provide you with a very good understanding of the domain."
https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3
All the videos for the Computer Vision lecture
on "Detection, Segmentation, and Tracking" are now public!
Videos: https://youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs…
Slides: https://dvl.in.tum.de/teaching/cv3dst-ss20/
on "Detection, Segmentation, and Tracking" are now public!
Videos: https://youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs…
Slides: https://dvl.in.tum.de/teaching/cv3dst-ss20/
In future #AI hiring other AI be like: Job Profile: *human baby sitter*
- Experience : trained on 100 years of past data.
- Test Accuracy : 99.9999
- Precision: blah
- recall : blah
- AUC : blah blah
- Inference time: A.C
- Trained on : Latest "alien" TPUs and GPUs
- Bias : blah Note: AI trained on old TPUs will not be considered. And then AI will gossip with each other about bias and discrimination they have to go through compared to others like:
- "Wouldn't I be considered if I am trained on X country's data?"
- "Why was she considered even though she has outliers in the data?"
- "I am trained on old TPUs, I won't be considered? What!" LOL #artificialintelligence #machinelearning
- Experience : trained on 100 years of past data.
- Test Accuracy : 99.9999
- Precision: blah
- recall : blah
- AUC : blah blah
- Inference time: A.C
- Trained on : Latest "alien" TPUs and GPUs
- Bias : blah Note: AI trained on old TPUs will not be considered. And then AI will gossip with each other about bias and discrimination they have to go through compared to others like:
- "Wouldn't I be considered if I am trained on X country's data?"
- "Why was she considered even though she has outliers in the data?"
- "I am trained on old TPUs, I won't be considered? What!" LOL #artificialintelligence #machinelearning
A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embeddings
• Using node2vec to extract features from a twitter follower graph. In conjunction with user features provided by Twitter.
This hybrid approach considers both the characteristics of the user and his social graph. The results show that the approach consistently and significantly outperforms existent approaches limited to user features.
Paper is.gd/LP9uKD
• Using node2vec to extract features from a twitter follower graph. In conjunction with user features provided by Twitter.
This hybrid approach considers both the characteristics of the user and his social graph. The results show that the approach consistently and significantly outperforms existent approaches limited to user features.
Paper is.gd/LP9uKD
Stanford CS224w’s lectures Machine Learning with Graphs, Leskovec et al.: https://lnkd.in/d4Cnahj #DeepLearning #Graphs #MachineLearning
Tensorflow + dalex, or how to explain a TensorFlow model
Article:
https://www.kdnuggets.com/2020/11/dalex-explain-tensorflow-model.html
Code:
https://dalex.drwhy.ai/python-dalex-tensorflow.html
Explanatory Model Analysis:
https://pbiecek.github.io/ema/
Article:
https://www.kdnuggets.com/2020/11/dalex-explain-tensorflow-model.html
Code:
https://dalex.drwhy.ai/python-dalex-tensorflow.html
Explanatory Model Analysis:
https://pbiecek.github.io/ema/
KDnuggets
tensorflow + dalex = :) , or how to explain a TensorFlow model
Having a machine learning model that generates interesting predictions is one thing. Understanding why it makes these predictions is another. For a tensorflow predictive model, it can be straightforward and convenient develop an explainable AI by leveraging…
The upcoming Saturday 10am CET we perform a SeaMapAI Project Meetup #1. The idea is to cycle GANs, utilize neural networks orchestration, evaluate images and eventually generate sea maps!
https://www.eventbrite.co.uk/e/introducing-sailmapai-educational-neural-seamap-generator-project-tickets-149691925579?aff=telegram
https://www.eventbrite.co.uk/e/introducing-sailmapai-educational-neural-seamap-generator-project-tickets-149691925579?aff=telegram
Eventbrite
Introducing SailMapAI and GetConf - Neural Seamap Generator & Mobile Apps
Introducing SailMapAI - Educational Neural Seamap Generator Project
If you are looking for educational opportunities, subscribe to this channel
👇👇
🔰 https://t.me/DLeX_Apply
👇👇
🔰 https://t.me/DLeX_Apply
If there is no signal in your data, the ML model won't magically be predictive.
Plus simpler models will do better with low signal vs. bigger more complex models...
Unfortunately, it is, what it is
Plus simpler models will do better with low signal vs. bigger more complex models...
Unfortunately, it is, what it is
he more you train deep learning models, the more you realize that there is still so much left to figure out with NNs..
Neural networks still
- require a lot of data cleaning
- need a fair bit of featurization
- regularly overfit
- often don't learn the nuance in the data
Neural networks still
- require a lot of data cleaning
- need a fair bit of featurization
- regularly overfit
- often don't learn the nuance in the data
Self supervised learning is the most intriguing form of AI yet..
Like babies, machine simply learn by observing the environment
Multi-task self learners that learn from hybrid inputs comprising of text, voice and images will be our next step towards AGI
Like babies, machine simply learn by observing the environment
Multi-task self learners that learn from hybrid inputs comprising of text, voice and images will be our next step towards AGI