An amazing new project from @bearpelican was just released: https://t.co/DBov6sZTVS . A beautiful design; you can auto-generate a melody from chords, chords from a melody, and more.
It's technically brilliant, combining BERT, seq2seq, and Transformer XL
https://t.co/jF3mO5aXiu
https://t.co/jF3mO5aXiu
https://t.co/DrCJxxJRAy
https://t.co/GgatWxa9nM
https://t.co/U9Yp5IZpxt
More from Data science
I have always emphasized on the importance of mathematics in machine learning.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
📘 Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
📘 Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
✨✨ BIG NEWS: We are hiring!! ✨✨
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩
👇 Here below a thread on who we are and what we
We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩💻 presentation for some examples:
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩
👇 Here below a thread on who we are and what we
We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩💻 presentation for some examples:
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
1/ A ∞-wide NN of *any architecture* is a Gaussian process (GP) at init. The NN in fact evolves linearly in function space under SGD, so is a GP at *any time* during training. https://t.co/v1b6kndqCk With Tensor Programs, we can calculate this time-evolving GP w/o trainin any NN
2/ In this gif, narrow relu networks have high probability of initializing near the 0 function (because of relu) and getting stuck. This causes the function distribution to become multi-modal over time. However, for wide relu networks this is not an issue.
3/ This time-evolving GP depends on two kernels: the kernel describing the GP at init, and the kernel describing the linear evolution of this GP. The former is the NNGP kernel, and the latter is the Neural Tangent Kernel (NTK).
4/ Once we have these two kernels, we can derive the GP mean and covariance at any time t via straightforward linear algebra.
5/ So it remains to calculate the NNGP kernel and NT kernel for any given architecture. The first is described in https://t.co/cFWfNC5ALC and in this thread
2/ In this gif, narrow relu networks have high probability of initializing near the 0 function (because of relu) and getting stuck. This causes the function distribution to become multi-modal over time. However, for wide relu networks this is not an issue.
3/ This time-evolving GP depends on two kernels: the kernel describing the GP at init, and the kernel describing the linear evolution of this GP. The former is the NNGP kernel, and the latter is the Neural Tangent Kernel (NTK).
4/ Once we have these two kernels, we can derive the GP mean and covariance at any time t via straightforward linear algebra.
5/ So it remains to calculate the NNGP kernel and NT kernel for any given architecture. The first is described in https://t.co/cFWfNC5ALC and in this thread
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1/ Some initial thoughts on personal moats:
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
I'm increasingly interested in the idea of "personal moats" in the context of careers.
— Erik Torenberg (@eriktorenberg) November 22, 2018
Moats should be:
- Hard to learn and hard to do (but perhaps easier for you)
- Skills that are rare and valuable
- Legible
- Compounding over time
- Unique to your own talents & interests https://t.co/bB3k1YcH5b
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
People talk about \u201cpassive income\u201d a lot but not about \u201cpassive social capital\u201d or \u201cpassive networking\u201d or \u201cpassive knowledge gaining\u201d but that\u2019s what you can architect if you have a thing and it grows over time without intensive constant effort to sustain it
— Andrew Chen (@andrewchen) November 22, 2018
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
Things that look like moats but likely aren\u2019t or may fade:
— Erik Torenberg (@eriktorenberg) November 22, 2018
- Proprietary networks
- Being something other than one of the best at any tournament style-game
- Many "awards"
- Twitter followers or general reach without "respect"
- Anything that depends on information asymmetry https://t.co/abjxesVIh9
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
1/“What would need to be true for you to….X”
Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?
A thread, co-written by @deanmbrody:
2/ First, “X” could be lots of things. Examples: What would need to be true for you to
- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal
3/ Normally, we aren’t that direct. Example from startup/VC land:
Founders leave VC meetings thinking that every VC will invest, but they rarely do.
Worse over, the founders don’t know what they need to do in order to be fundable.
4/ So why should you ask the magic Q?
To get clarity.
You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.
It also holds them (mentally) accountable once the thing they need becomes true.
5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”
Multiple responses to this question are likely to deliver a positive result.
Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?
A thread, co-written by @deanmbrody:
Next level tactic when closing a sale, candidate, or investment:
— Erik Torenberg (@eriktorenberg) February 27, 2018
Ask: \u201cWhat needs to be true for you to be all in?\u201d
You'll usually get an explicit answer that you might not get otherwise. It also holds them accountable once the thing they need becomes true.
2/ First, “X” could be lots of things. Examples: What would need to be true for you to
- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal
3/ Normally, we aren’t that direct. Example from startup/VC land:
Founders leave VC meetings thinking that every VC will invest, but they rarely do.
Worse over, the founders don’t know what they need to do in order to be fundable.
4/ So why should you ask the magic Q?
To get clarity.
You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.
It also holds them (mentally) accountable once the thing they need becomes true.
5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”
Multiple responses to this question are likely to deliver a positive result.