Introduction to Deep Learning
Slides, course materials, demos, and implementations
https://chokkan.github.io/deeplearning/

ISSCC2018 - 50 Years of Computer Architecture:From Mainframe CPUs to Neural-Network TPUs


https://www.youtube.com/watch?v=NZS2TtWcutc

Theorizing from Data by Peter Norvig (Video Lecture)

https://catonmat.net/theorizing-from-data-by-peter-norvig-video-lecture

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Introducing TensorFlow Datasets

By TensorFlow: https://lnkd.in/d2yEjSr

#MachineLearning #Data #Dataset #TensorFlow

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6 Tips to Improve Your Code for Data Science (with links)

1. Strictly follow style standards
-> https://lnkd.in/gKZUjVa

2. Use a linter to enforce style standards
-> https://lnkd.in/d_prybR

3. Write modular, generic, object-oriented code -
-> https://lnkd.in/gsynW6Q
-> https://lnkd.in/dx53u53

4. Write unit tests to test your functions and methods
-> https://lnkd.in/dsy-bPu

5. Organize your code base
-> https://lnkd.in/dviGffH

6. Separate exploration and production development, and develop production code using test-driven development (TDD)
-> https://lnkd.in/dMn-s32


If you'd like some real code examples, I've got 5 end-to-end data science projects with instructions, data, code, and complete video walkthroughs as part of the DSDJ course.

These examples and videos will walk you through everything that you need to take your data science coding skills to the next level.

To learn more, join our mail list today at https://lnkd.in/g7AYg72

#datascience #programming

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TensorBoard now works in Jupyter Notebooks, via magic commands "%" that match the command line.

Example: https://lnkd.in/dfGH2gH

H / T : Josh Gordon

#artificialintelligence #deeplearning #tensorflow

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Visualizations are one of the best ways of telling a story with data. They are extremely useful when trying to understand data and unearth hidden patterns. Check out these 4 articles to design mind-blowing visualizations:

A Collection of 10 Data Visualizations You Must See - https://lnkd.in/fRemdbn

How to create Beautiful, Interactive data visualizations using Plotly in #R and #Python - https://lnkd.in/fN3e9m8

Comprehensive Guide to #DataVisualization in R - https://lnkd.in/fw9M-De

R-analyst #Cheatsheet: Data Visualization in R - https://lnkd.in/fnakeqH

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Data scientists didn’t invent imposter syndrome, but we’re really good at it. Vagueness of what a data scientist does is a big contributor. My co-authors and I tried to clarify.
https://lnkd.in/e9M2v-X

Here's a blog post building on it.
https://lnkd.in/eDAUy4a

Also, here’s an imposter syndrome pep talk especially for data scientists. I hope it helps.
https://lnkd.in/drv-hfX

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What skills should you start learning to become a data scientist?

I've had a lot of people ask me that recently, but it's really the wrong question.

Try this instead -

➡️ 1. Choose a problem (and dataset) that you find interesting
➡️ 2. Begin trying to solve the problem
➡️ 3. When you get stuck because your skill set is limited, go learn that skill

For example, if you get stuck...
- loading the data into a dataframe/table, then learn pandas or SQL
- identifying outliers, then study stats
- figuring out what to do with missing data, then learn when to remove data, replace values, impute values, etc
- building a regression model that makes good predictions, then learn about regression techniques

👉 There are 2 big differences when you take this approach:

1. You don't waste time guessing what you need to know
2. You're highly motivated to learn at every step of the way

This eliminates the scenario where you're learning a subject because someone else told you it's a good idea.

👉 Now you're learning because you NEED that knowledge for something useful.

Start using this approach in your studies and you'll also see that you're learning more quickly and completely.

#datascience #aspiring #datascientist

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PyTorch under the hood

By Christian S. Perone: https://lnkd.in/diyNcYU

#artificialintelligence #deeplearning #pytorch

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Best article I've read so far on understanding how Neural ODEs work. Well and clearly explained by solving a regression problem. If you have read the paper (Link: https://lnkd.in/dfDnJJz) and had difficulties understanding it then you should definitely read this blog post. #deeplearning #machinelearning

Link: https://lnkd.in/dhMhwyb

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The Matrix Calculus You Need For Deep Learning

By Terence Parr and Jeremy Howard : https://lnkd.in/dC5MqZM

#ArtificialIntelligence #BigData #DeepLearning #MachineLearning #NeuralNetworks

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HOW TO IMPROVE YOUR SKILL ON TEXT DATA?

Rubens Zimbres, PhD compile amazing resources on Machine Learning, NLP, and Computer Vision. On NLP Side he cover pretty much every common topic on NLP, this is very useful because as data scientist we often dealing with text data.

Yo can see the repository here https://lnkd.in/fyyvZYt

#repository #machinelearning #patternrecognition #artificialintellegence

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Your Data Strategy is your AI Strategy

Unless you’re one of the massive digital-first companies your AI strategy needs to address these truths:

1. Right now Artificial Intelligence is really Machine Learning. Comprised of three elements; traditional ML (e.g., XG-Boost, SVM, etc.), Deep Learning, Reinforcement Learning
2. You’re not going to create new algorithms that significantly advance the science of ML. This is because it’s very difficult to compete with the likes of Google, Apple, etc. for the best algorithm developers and the billions they are investing in ML.
3. All of your competitors are working on their AI/ML strategy and soon your investments in AI/ML will be table stakes, not a sustainable competitive advantage.

The only way to compete in the above environment is to focus on your one area of competitive advantage; your data. Whether is be customer or internal process data, it is the first place to go for creating value and to do that you’ll need a data strategy.

More on data strategy in my next post.


#artificialintelligence #machinelearning #digitization #datascience


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100 Days of Machine Learning Coding as proposed by Siraj Raval

#tutorial #machine_learning #python #infographic #100DaysOfMLCode
@pythony

github.com/Avik-Jain/100-Days-Of-ML-Code

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A large, biased sample in some ways is worse than a small, biased sample. It will be more credible in the eyes of many decision-makers by virtue of its size and more analytic damage can be done with it - weak associations may be statistically "significant" in large samples and many analytic procedures will not run on very small samples, for example.

The potential silver lining is when the data contain information that helps us understand and, possibly, adjust for sources of bias. This is much easier to accomplish when the sample is large.

How big is large? This depends on many things, such as the number of variables that will be used in the analysis and, more importantly, the purpose of the research.

In general, however, statisticians consider a sample of a few hundred "large" by historical definitions.

A "sample" of a few dozen that actually represents the population avoids having to make statistical inferences about the population from the sample, though often researchers are really generalizing beyond that population, e.g., to employees at the same company in five years. "Populations" can be elusive. Moreover, there are analytic limitations to very small data even when it is not a sample.

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Make music with GANs!

GANSynth is a new method for fast generation of high-fidelity audio.

🎵 Examples: https://lnkd.in/enSDBJZ
⏯ Colab: https://lnkd.in/eDt_S3w
📝 Paper: https://lnkd.in/eCsZbx2
💻 Code: https://lnkd.in/eN3B5xc
⌨️ Blog: https://lnkd.in/eXks33i

Join Us and Share With Your Friend

#artificialintelligence #deeplearning #generativeadversarialnetworks

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HOW TO WRITE BETTER CODE FOR DATA SCIENCE

1. Strictly follow style standards
-> https://lnkd.in/gKZUjVa

2. Use a linter to enforce style standards
-> https://lnkd.in/d_prybR

3. Write modular, generic, object-oriented code -
-> https://lnkd.in/gsynW6Q
-> https://lnkd.in/dx53u53

4. Write unit tests to test your functions and methods
-> https://lnkd.in/dsy-bPu

5. Organize your code base
-> https://lnkd.in/dviGffH

6. Separate exploration and production development, and develop production code using test-driven development (TDD)
-> https://lnkd.in/dMn-s32

This list curated by DSDJ Founder, Kyle McKiou

If you'd like some real code examples, DSDJ got 5 end-to-end data science projects with instructions, data, code, and complete video walkthroughs as part of the course.

These examples and videos will walk you through everything that you need to take your data science coding skills to the next level.

#datascience #programming

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

MIT Introduction to Deep Learning

MIT 6.S191 Introduction to Deep Learning: https://lnkd.in/e2qmSWR

#artificialintelligence #deeplearning #machinelearning #tensorflow

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Math Blocking you from doing Machine Learning?

#machinelearning

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"When you first study a field, it seems like you have to memorize a zillion things. You don't. What you need is to identify the 3-5 core principles that govern the field. The million things you thought you had to memorize are various combinations of the core principles." -J. Reed

“1. Multiply things together
2. Add them up
3. Replaces negatives with zeros
4. Return to step 1, a hundred times” - Jeremy Howard

#artificialintelligence #deeplearning #machinelearning

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If you want to learn the basics of deep learning and also TensorFlow, then check out MIT's introductory course on deep learning. The complete lecture series is now online with a lot of code examples for TensorFlow 2.0. The course covers a wide range of topics (Computer Vision, GANs, NLP etc) and there are also guest lectures from Google, NVIDIA etc. The code is designed to run seamlessly on Google colab so very easy then. Check it out! #deeplearning #machinelearning

Article: https://lnkd.in/d7yT6dU
Course page: https://lnkd.in/deaTRDZ
Github code: https://lnkd.in/d-jwcPW

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