[ANN] core.rrb-vector 0.1.1 -- eliminate a warning in ClojureScript version

2019-10-25 Thread Andy Fingerhut 
I am pleased to announce the 0.1.1 release of core.rrb-vector, a Clojure
Contrib library extending the Clojure vector API with logarithmic-time
concatenation and slicing:

  https://github.com/clojure/core.rrb-vector

  [org.clojure/core.rrb-vector "0.1.1"]

  
org.clojure
core.rrb-vector
0.1.1
  

This release is nearly identical to the recent version 0.1.0 release.  The
only notable change is for the ClojureScript implementation, to avoid
causing the ClojureScript compiler to issue a warning.  I am not aware of
any functional bugs due to that warning, but it seemed best to clean up the
code there anyway.

Independent of that, I have added some benchmarks and extra documentation
related to the library since the 0.1.0 release, described in the change log:

  https://github.com/clojure/core.rrb-vector/blob/master/CHANGES.md

Cheers,
Michał Marczyk and Andy Fingerhut

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[ANN] Cognitect Labs' aws-api 0.0.8.391

2019-10-25 Thread David Chelimsky 
Cognitect Labs' aws-api 0.8.391 is now available!

CHANGES

   - Fix: S3 HeadObject fails with large files #97 
   
  - This was fixed in cognitect/http-client 0.1.101
   - Fix concurrency 4 limit introduced in 0.8.383
   
README: https://github.com/cognitect-labs/aws-api/API Docs: 
https://cognitect-labs.github.io/aws-api/
Changelog: https://github.com/cognitect-labs/aws-api/blob/master/CHANGES.md
Latest Releases of api, endpoints, and all services: 
https://github.com/cognitect-labs/aws-api/blob/master/latest-releases.edn

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Re: Keys in EDN maps: keywords or symbols

2019-10-25 Thread James Reeves 
As you point out, symbols in edn have no inherent meaning; it's entirely up
to how you interpret them. However, if you saw the symbol
"clojure.core/conj" in an edn file, then the chances are that it's intended
to refer to a Clojure function. Similarly, the symbol "PI" likely refers to
the mathematical constant.

A good litmus test is to replace the symbol with the thing it identifies,
and then see if the data still makes sense. So if we take your example:

{:angle PI}

We can substitute the symbol for the data it identifies:

{:angle 3.141592653589793}

And the edn file still makes sense. It's lost no significant semantic
meaning. However, if we return to your earlier example:

{username "vasya"}

What external identity does the symbol "username" have? If we say that it's
"vasya", then the substitution test fails:

{"vasya" "vasya"}

Since "username" has no meaning beyond itself, it should ideally be a
keyword:

{:username "vasya"}


On Fri, 25 Oct 2019 at 15:30, Anton Vodonosov  wrote:

> Thank you for responses.
>
> The last two arguments do not resolve the doubt, however:
>
> 1. symbols also implement IFn:
>
> (require 'clojure.edn)
> ('a (clojure.edn/read-string "{a 1 b 2}"))
> => 1
>
> 2. EDN does not specify any evaluation model, so how can a symbol
> designate something "in isolation"?
> For example, clojure.core/conj in the context of EDN does not
> designate a function - EDN is not Clojure.
>
>>
> One way I can imagine symbols to designate something is by agreement
> of particular EDN config file format.
> Maybe a user can specify {:angle PI}, and this specific config file
> format declares that the PI symbol
> can be used to refer the 3.14... constant.
>
> Still, I'm not sure such interpretation implies symbols in map keys
> should be resolved by the config file parser
> to some values
>
>
>
>
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> .
>


-- 
James Reeves
booleanknot.com

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Re: Keys in EDN maps: keywords or symbols

2019-10-25 Thread Anton Vodonosov 
Thank you for responses.

The last two arguments do not resolve the doubt, however:

1. symbols also implement IFn:

(require 'clojure.edn)
('a (clojure.edn/read-string "{a 1 b 2}"))
=> 1
 
2. EDN does not specify any evaluation model, so how can a symbol designate 
something "in isolation"?
For example, clojure.core/conj in the context of EDN does not designate 
a function - EDN is not Clojure.

>
One way I can imagine symbols to designate something is by agreement of 
particular EDN config file format.
Maybe a user can specify {:angle PI}, and this specific config file 
format declares that the PI symbol
can be used to refer the 3.14... constant.

Still, I'm not sure such interpretation implies symbols in map keys 
should be resolved by the config file parser
to some values




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Deep Learning for Programmers 0.11.0 (Clojure AI Book WIP)

2019-10-25 Thread Dragan Djuric 
New release:Deep Learning for Programmers: An Interactive Tutorial with 
CUDA, OpenCL, MKL-DNN, Java, and Clojure

https://aiprobook.com/deep-learning-for-programmers 


+ Chapter on Adaptive Learning Rates

**
no middleman!
100% of the revenue goes towards my open-source work!

** this is the only DL book for programmers

- interactive & dynamic
- step-by-step implementation
- incredible performance, yet no C++ hell (!)
- Intel & AMD CPUs (MKL-DNN)
- Nvidia GPUs (CUDA and cuDNN)
- AMD GPUs (yes, OpenCL too!)
- Clojure (it’s magic!)
- Java Virtual Machine (without Java boilerplate!)
- complete source code
- beautiful typesetting (see the sample chapters)

Current status:

## Table of Contents

### Part 1: Getting Started

4-6 chapters, (TO BE DETERMINED)

### Part 2: Inference ([AVAILABLE])

 Representing layers and connections ([AVAILABLE])

 Bias and activation function ([AVAILABLE])

 Fully connected inference layers ([AVAILABLE])

 Increasing performance with batch processing ([AVAILABLE])

 Sharing memory ([AVAILABLE])

 GPU computing with CUDA and OpenCL ([AVAILABLE]

### Part 3: Learning ([AVAILABLE]

 Gradient descent and backpropagation ([AVAILABLE]

 The forward pass ([AVAILABLE]

 The activation and its derivative ([AVAILABLE]

 The backward pass ([AVAILABLE]

### Part 4: A simple neural networks API ([AVAILABLE]

 Inference API ([AVAILABLE]

 Training API ([AVAILABLE]

 Initializing weights ([AVAILABLE]

 Regression: learning a known function ([AVAILABLE]

### Part 5: Training optimizations (IN PROGSESS)

 Weight decay ([AVAILABLE]

 Momentum and Nesterov momentum ([AVAILABLE]

 Adaptive learning rates ([AVAILABLE]

 Regression: Boston housing prices (SOON)

 Dropout (SOON)

 Stochastic gradient descent (SOON)

 Classification: IMDB sentiments (SOON)

### Part 6: Tensors (TO BE DETERMINED, BUT SOON ENOUGH)

 Tensors, Matrices, and ND-arrays (TBD)

 Tensors on the CPU with MKL-DNN (TBD)

 Tensors on the GPU with cuDNN (TBD)

 Tensor API (TBD)

### Part 7: Convolutional layers (TBD)

4-6 Chapters, (TBD)

### Part 8: Recurrent networks (TBD)

4-6 Chapters, (TBD)

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