In #NLP, completed different text preprocessings in Spacy and NLTK, diffrent types of word and sentence vectorizations, and re library.

Now yet to do document and sentence classification and clustering.

✴️ @AI_Python_EN

A thread on data science generalists-vs-specialists.

Takeaways:
1) You don't have to know everything.
2) It's OK to specialize in an area you love.
3) Saying "I only build models" is the moral equivalent of licking the frosting off all the cupcakes.

https://lnkd.in/eAHQavr

✴️ @AI_Python_EN

Alex Smola is freely publishing his 639-pages long PDF book on Deep Learning. An absolute marvel which would be a great companion to my own upcoming book www.interviews.ai

http://d2l.ai/

✴️ @AI_Python_EN

Data Science with R Cognitive Classes

DO NOT SKIP THE LAB EXERCISE. IT IS VERY HELPFUL

Here is the sequence we should follow -

R101
https://lnkd.in/fXTKq5U

Using R with Databases
https://lnkd.in/fRrBV7N

Data Visualization with R
https://lnkd.in/fWT8ZVK

Machine Learning with R
https://lnkd.in/fERn7eT

Data Analysis with R is coming soon on Cognitive.
Here is the link
https://lnkd.in/fm-BSaS

#dataanalysis #datavisualization #datascience #machinelearning

✴️ @AI_Python_EN

Self Attention GAN is a image generative model which published in 2018. My project aims to generate high resolution and vivid Hearthstone cards using PyTorch. Self attention map and model training details have been visualised in tensor-board.

Repository: https://lnkd.in/fA4uMYZ
Paper: https://lnkd.in/ff6pnuj

#AI #deeplearning #GAN
#computervision

✴️ @AI_Python_EN

Data Science Lifecycle



by Global Tech Council #datascience

✴️ @AI_Python_EN

If you’re learning #datascience or #analytics, then really take the time to understand the art of making the complex simple.

No matter where you go, the data that you use for your:
- visualizations
- machine learning
- statistical analysis
- presentations

All boils down to how well you can communicate the results.

So build the habit on documentation, storytelling, and simplifying your thoughts on papers.

Spend that extra time to articulate your thoughts and think deeply on how you want to present your data.

Because that’s a skill that will always be needed in any place you go.

And not only will you thank yourself for doing this in the future, but your team will love you for making it so simple for them. 🙂

Also, who doesn’t love a simple and meaningful story.

#machinelearning #storytelling #communication

✴️ @AI_Python_EN

https://lnkd.in/edT8m4y #machineleaning
“PyText is a deep-learning based NLP modeling framework built on PyTorch”

For those unfamiliar with the man: Schmidhuber is one of the creators of LSTM. Well worth your time.
https://lnkd.in/dYgXxMm

✴️ @AI_Python_EN

Machine learning models are being increasingly used to make decisions that affect people’s lives. With this power comes a responsibility to ensure that the model predictions are fair and not discriminating.

#machinelearning #bias
https://towardsdatascience.com/is-your-machine-learning-model-biased-94f9ee176b67?gi=d3c7b468df95

✴️ @AI_Python_EN

Check out this beautifully drawn notes from the excellent Coursera specialization by Andrew Ng!

Source: https://lnkd.in/dw5_Fmm

✴️ @AI_Python_EN

▪️Data Science Projects:

☆ The Data Science IPython Notebooks:
=> This repository is filled with IPython notebooks that cover different topics, going from Kaggle competitions to big data and deep learning.
[ https://lnkd.in/dW3WBi6 ]

☆ The Pattern Classification:
=> Tutorials and examples to solve and understand machine learning and pattern classification tasks.
[ https://lnkd.in/d9PGxHm ]

☆ Deep Learning In Python:
=> This repository is the way to go!
[ https://lnkd.in/d-hNVCD ]

More ...
▪️Data Science News
▪️Data Science Books
▪️Data Science Talks
▪️R for Data Science Talks
▪️Python for Data Science Talks
▪️Big Data Talks
▪️Data Science Podcasts
▪️Data Science Webinars
▪️Data Science Tutorials
▪️Data Science Community
▪️Data Science Courses

Refer to the full article
[ https://lnkd.in/dmKmx_D ]

✴️ @AI_Python_EN

Although there have been notable exceptions, quantitative marketing research on the whole has historically been quite basic.

In some respects, MR is still living in the early 90s, just before data warehousing, data mining and predictive analytics began to boom - developments that largely bypassed or were ignored by MR.

Though AI, machine learning, automation, etc., are now the buzz in MR, many marketing researchers are struggling to adapt the new world of marketing and marketing analytics.

52 Things About Customer Analytics (Sherman and Sherman) is a good place to start if you'd like to learn more about some of these new developments. Data Mining Techniques (Linoff and Berry) and Introduction to Algorithmic Marketing (Katsov) delve into these and related topics in more detail.

Data Science and Advanced Analytics aren't for everyone, though, and we need to bear this in mind. Many marketing researchers who have leaned on selling and people skills over the years are going to find the road ahead very bumpy. More and more, you have to be able to walk the walk if want to talk the talk.

✴️ @AI_Python_EN

DeepMind and Google: the battle to control artificial intelligence

Blog by Hal Hodson: https://lnkd.in/eVYhX2e

#artificialintelligence #deeplearning #reinforcementlearning

✴️ @AI_Python_EN

Did you know that Google has recently open-sourced their federated learning framework as part of the TensorFlow ecosystem? It's called TensorFlow Federated or TFF. They also created a simple example which uses the classic MNIST dataset. In particular, they're simulating the data on each client devices and then sample a random subset of the clients that are involved in the training, usually in multiple rounds. The cool thing is that we can use Keras to construct a model and then it can be plug into TFF seamlessly. Very cool! Check it out! Code is also provided! #deeplearning #machinelearning

Article: https://lnkd.in/dM-upwn
TF Federated Learning Page: https://lnkd.in/d4FnthY
Tutorial: https://lnkd.in/dw5DMUW

✴️ @AI_Python_EN

François Chollet Tweeted:
Are you a deep learning researcher? Wondering if all this #TensorFlow 2.0 stuff you heard about is relevant to you?

This thread is a crash course on everything you need to know to use TensorFlow 2.0 + Keras for #deeplearning research. Read on! https://t.co/dFNI2E6yjF https://lnkd.in/fiqShbx

✴️ @AI_Python_EN

An All-Neural On-Device Speech Recognizer #DataScience #MachineLearning #ArtificialIntelligence http://bit.ly/2VWExJQ

✴️ @AI_Python_EN

Creating a good machine learning powered product is a team sport. There is not one unicorn 🦄 that can do it all but you need several roles:

1. 👔 business -> product (product manager/designer)
2. 💡 product -> ML problem (product manager/designer/research scientist)
3. 📚 ML problem -> research paper (research scientist)
4. 👨🏻‍💻 research paper -> code (research scientist/research engineer)
5. 🤖 code -> production (research engineer/data engineer/software engineer)
6. 🔁 repeat 1-5

So don't make the mistake to hire one data scientist and hope that you will become an AI company tomorrow. #deeplearning #machinelearning

✴️ @AI_Python_EN

Before buying or purchasing GPU, do take a look at how they perform do always take a look at what is the balance between what you pay and how much compute power you get.

#deeplearning #computation #performance #gpu

❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

🔸 100 Days of ML Coding
#Machine_Learning #Ml
#machine_learning_algorithms

👉 Link Review

〰️〰️〰️〰️〰️
©️ @Code_Community

WANT TO UNDERSTAND MACHINE LEARNING BETTER? LEARN IT VISUALLY AND TRY SOME PROJECTS

1. LEARN IT VISUALLY
Visual speak louder. Visualization helps us to imagine things and and retain a concepts.

This is will useful to Machine learning concepts. People can easily grasp the idea of an algorithm when presented in an interactive way.

R2D3(http://www.r2d3.us/) is build statistical thinking with interactive design. R2D3's goal is to make machine learning accessible to a broad group of people, including folks who don't speak English. #machinelearning #datascience

You can see here http://www.r2d3.us/

2. TRY SOME PROJECTS

Here's some projects taht writrn by Harun-Ur-Rashid (Shimanto) for his #100DaysMLCOde, you can start also
a.Naive Bayes (NB)(https://goo.gl/xSJZCZ)
b.Sentiment Analysis (https://goo.gl/xSJZCZ)
c. Support Vector Machine(SVM)(https://goo.gl/xSJZCZ)
d. Artificial Neural Networks (ANN)(https://goo.gl/xSJZCZ)
✴️ @AI_Python_EN