Not directly, but have been following various projects over the years..
My take on this is that the people using CL for machine learning have been doing this for some time, and so have their own toolsets; tensor flow is relatively new in that regard, and w/r/t lisp interfacing would entail low-level binary interfacing (and therefore mostly non-portable between implementations) to hook CL code with tensorflow kernels (definately not an expert here on either however).
also, what is popularly referred to as 'machine learning' is in my opinion mostly
only one aspect of the field - e.g. classification neural networks - while lisp can
definitely do this, lisp AI programming (in my amateur opinion) shines more in the realm
of machine reasoning/inference due to the symbolic / dynamic nature of the environment - e.g. constructing a set of reasoning primitives (e.g. functions and facts and decision trees) and a meta-interpreter to reason/infer about external data and walk around a problem space.. also, owing to the dynamic and rapid development nature of the language, likely many people are working with their own prototype/core frameworks, possibly cobbled together from various small bits and pieces of 3rd party code. Also, neural networks have been around for quite a while - what these new frameworks bring to the
table is not so much new core algorithms, but the ability to quickly cobble them together in a more popular/user friendly way, and also take advantage of fast hardware (e.g. GPUs)
as for projects - in the general sense, lisp has been a latecomer to the 'languages with cpan-style trove of public addon modules' crowd, owing in my opinion to the need to support multiple implementations in order for such a project to take hold - so older but yet still quite functional libraries might be around in various hodge-podge repositories which are not standardised but old timers have already included in their own local systems, etc. (see also CMU AI repository)
In the last few years, much has been done in the module space - I would
definately consider 'quicklisp' to be roughly the defacto definitive list of current modules, especially those under active development, since the active community is basically converging on this as a module/distribution platform and so many (most?) active community projects are available as quicklisp modules and included here - and so would probably be the one of the first places to start in checking into for available libraries for any topic.
Also, the best way to 'explore' quicklisp is to install it, and then install various packages and then muck around/explore with the source code that they download into
your environment - the documentation tends to be much less 'external' (e.g. websites) and much more 'internal' (e.g. READMEs, in-tree code examples or unit tests).
https://www.quicklisp.org/beta/releases.html
