So, you’ve decided you want to be #datascientist.

If you’re like me, navigating the #datascience landscape of different terminology, courses, and misconceptions about the discipline, itself, can be overwhelming. When you get distracted or worried about trying to keep up with everyone else, it becomes even more daunting.

We all come from unique backgrounds and are in different places on our #datasciencejourney. If you find yourself getting frustrated, pause, take a deep breath, and:

1. Level-set on your goals. Remind yourself what you really want to accomplish and / or where you hope to be in the future.

2. Take stock of where you are at right now and where you have been. Determine the gaps, and come up with a plan to fill them.

3. Stop worrying about how many / what types of certifications, degrees, courses, skills, and / or jobs everyone else has. Focus on you!

4. Remember life is a marathon, NOT a sprint. Celebrate quick-wins, but be patient with yourself. Enjoy the journey.

As a natural competitor, this has been one of the hardest lessons I’ve had to learn, and I still struggle.

What other advice would you give to those that are new to #datascience and / or people who have had some experience with data, but want to make the leap to #datascientist?

I'd like to add 'Specify where you want to go'.
There are so many different 'data scientists' from data analyst to deep learning engineers. So Be more specific in what field you want to go in, not being a generalist. Highly recommend this article by Jeremie Harris
https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c

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PyTorch implementation of DRAW: A Recurrent Neural Network For Image Generation

Code by Mohit Jain: https://lnkd.in/eSQkBNj

#artificialintelligence #deeplearning #pytorch #neuralnetworks

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"A Few Useful Things to Know about Machine Learning". Very neat article by Pedro Domingos!
https://lnkd.in/gyyUHVZ
#machinelearning

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The 6 most useful Machine Learning projects of the past year (2018)


https://lnkd.in/dsjax4W
#machinelearning

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Deep RL Bootcamp

By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://lnkd.in/edFXgDP

#ArtificialIntelligence #DeepLearning #MachineLearning #ReinforcementLearning

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I stepped away from my usual Python modules like #Numpy,#Pandas, #Matplotlib, #Scikit, etc. and ventured into 20 Python modules and APIs I rarely use or have never worked with before, including Poliastro for orbital mechanics, #biopython for Computational Molecular Biology, #pandas_datareader for financial data and stock information and #algorithms for implementing well known CS algorithms.


What I learned:

1. Coding is actually really fun. It seems like a chore when you treat it like a means to a good career or a way to become the next billionaire or world problem solver.

2. I know some people only think of Python in terms of a Data Science, ML or AI sense but a careful investigation would show Python is actually a very thriving language in CS with a very active community that is probably rivalled only by the Linux community in my opinion.

3. Documentation is what separates good code from great code. Others should be able to read your code, use it and contribute meaningfully to it.

4. If you want to understand Object Oriented Programming really well, experiment with Python modules and try to see if you can contribute to a particular module or create one on your own.

Github: https://bit.ly/2F5ezQ1

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Very cool book on utilizing the concepts of TDD to test and improve your machine learning models and automate the entire process.

Check it out.

#datascience #machinelearning

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Writing Code for NLP Research

Slides by the Allen Institute for Artificial Intelligence: https://lnkd.in/eubgSGY

#naturallanguageprocessing #NLP #research

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Here are three nice posts/pages regarding Gaussian Processes which help illuminate some Bayesian concepts.
#datascience

Gaussian Processes are Not so Fancy
https://bit.ly/2F8jaRu
(Python implementation)

A Visual Exploration of Gaussian Processes
https://bit.ly/2BQGwXA
(Cool interactive features)

Robust Gaussian Processes in Stan
https://bit.ly/2COMgme
(R implementation using stan library)

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I won't claim to be an authority on neural nets, but here are some books on ANN I can recommend:

- Neural Network Design (Hagan)

- Deep Learning and Neural Networks (Heaton)

- Deep Learning (Goodfellow et al.)

- Deep Learning with R (Chollet and Allaire)

- Neural Networks and Deep Learning: A Textbook (Aggarwal)

- Neural Network Methods in Natural Language Processing (Goldberg)

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10 Predictions for #DeepLearning in 2019 – Intuition Machine

https://bit.ly/2F26VG4

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GitHub Free users now get unlimited private repositories
link
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#DeepSpeech --> how a speech application works.

Project DeepSpeech is an open source Speech-To-Text engine, using a model trained by machine learning techniques, based on Baidu, Inc.'s Deep Speech research paper.

Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.

Paper: https://lnkd.in/di6kSyB
Github: https://lnkd.in/dka5rWn

#ArtificialIntelligence #NLP #speechrecognition #DeepLearning #machinelearning

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How would a rockstar 🎸would improve their machine learning models?
چگونه مدلهای یادگیری ماشین را بهبود دهیم؟

To get better at playing the guitar, you play the guitar more. You try different songs, different cords. Practice, practice, practice.

All the practice adds up to more experience, more examples of different notes.

And to try something totally different, you might merge two songs together. Or even take a song written originally for the piano but play it on your guitar.

After a while, you're ready to play a show. But the show won't some any good if all the speakers are set to different settings. Steve the sound guy takes care of this.

How does this relate #machinelearning?

1. More practice = more data

More examples of playing different notes = more data. Machine learning models love more data.

2. Combining different songs = feature engineering

If the #data you have isn't in the form you want, transforming into a different shape may be a better way of looking at it.

3. Tuning the speakers = hyperparameter tuning

There's a reason tuning the speakers is the last step in playing a rock show. Working speakers don't mean anything without all the practice (collecting data) and songwriting (feature engineering). If you've done 1 and 2 right, this is the easy part.

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Here is another Learning path of Deep Learning. Check it out.


Link to complete article: https://lnkd.in/fQTtuex

#deeplearning


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What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning

Paper by Gordon et al.: https://lnkd.in/eVqQ4BB

#artificialintelligence #machinelearning #reinforcementlearning

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Great new resource with code, math, explanations, textbook, upcoming videos: Berkeley's Spring 2019 Introduction to Deep Learning

These are the topics:

- A Taste of Deep Learning

- Deep Learning Basics

- Deep Learning Computation

- Convolutional Neural Networks

- Recurrent Neural Networks

- Optimization Algorithms

- Computational Performance

- Computer Vision

- Natural Language Processing

https://lnkd.in/fhVEJWm

#deeplearning #machinelearning #artificialintelligence

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CS224n: Natural Language Processing with Deep Learning
#NLP #deeplearning
Stanford / Winter 2019

Public lecture videos: Once the course has completed, we plan to also make the videos publicly available on YouTube.

http://web.stanford.edu/class/cs224n/


This year, CS224n will be taught for the first time using #PyTorch rather than #TensorFlow

🙏Thanks to: @cyberbully_gng
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A guide to deep learning in healthcare

https://www.nature.com/articles/s41591-018-0316-z

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Artificial Intelligence used to create inexpensive heart disease detector.

https://bit.ly/2CXeewh

#IoT #BigData #MachineLearning #ML #fintech #tech #blockchain #DeepLearning #DataScience #cyberecuin



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Facilitate proactive #cybersecurity threat hunting, detection, & analysis with game-changing technical capabilities (#BigData Analytics + #MachineIntelligence)

https://www.oreilly.com/ideas/modernizing-cybersecurity-approaches

#DataScience #BehavioralAnalytics #MachineLearning #AI

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