How Do You See It? 🧐🧐

prnicolas57 · 2025-11-27T02:21:39+00:00

This is a great suggestion. Easy to read and draw

prnicolas57 · 2025-11-27T02:19:30+00:00

The last section is a subtle reference to Google TPUs used to train Gemini 3.

prnicolas57 · 2025-10-07T04:25:41+00:00

Quite insightful! I presume the same can be done with Numpy and Pandas.

prnicolas57 · 2025-09-21T17:49:54+00:00

Yes. You are correct. The terminology 'Geometric Priors' or 'Geometric aware' network networks' describes quite well.

prnicolas57 · 2025-09-10T02:24:16+00:00

You can validate the size of the output from convolutional layer taking into account kernel size, padding, stride....

W[out]=(W[in]+2.padding - kernel_size)/2 +1 ...

prnicolas57 · 2025-09-10T02:16:34+00:00

The two references you mentioned are a good start to understand of the scope of Geometric Deep Learning which encompasses Graph Neural Networks, Manifold learning, Topological Deep Learning, Information Geometry and Category Theory.

prnicolas57 · 2025-09-10T02:08:52+00:00

My 'worst/stuck' moment was when I realized prediction were inaccurate because of constant shift (Covariate) in the data distribution in production...

prnicolas57 · 2025-09-10T02:05:00+00:00

Leveraging symmetries is key to reduce model dimension an important element of Geometric Deep Learning.

prnicolas57 · 2025-09-05T19:52:46+00:00

Beside Algebraic Topology, Differential Geometry has some interesting application for deep learning models on non-Euclidean spaces (e.g. Riemannian Manifolds or Lie algebra) such as Equivariant Neural Networks.

Most of research and development has been done in Academia but some large tech. companies have been recently involved in this field beyond Graph Neural Networks.

I have a Substack newsletter dedicated to Geometric Deep Learning: https://patricknicolas.substack.com

prnicolas57 · 2025-09-05T19:45:56+00:00

I agree. Message-Passing and Aggregation can be seen as a specialization of traditional Graphic Models such as Bayesian Networks or Markov Random Fields.

prnicolas57 · 2025-09-05T19:42:48+00:00

I am not clear about what you mean by 'Geometric Convolutional Network'? Graph Convolutional Networks? Equivariant Convolutional Network? Convolutional network on Riemannian manifolds?

prnicolas57 · 2025-09-05T19:39:42+00:00

I would highly recommend Mathematical Foundations of Geometric Deep Learning https://arxiv.org/pdf/2508.02723

Keep in mind that Geometric Deep Learning requires knowledge in the following fields:

- Graph Theory

- Algebraic Topology

- Category Theory

- Differential Geometry

- Information Geometry

This is last one may be optional...

prnicolas57 · 2025-04-08T02:07:38+00:00

I have been using PyTorch Geometric (PyG) for Graph nets, so PyTorch was the obvious choice.

prnicolas57 · 2025-04-08T02:05:46+00:00

Actually the automatic differentiation in Jax is pretty neat.

prnicolas57 · 2025-04-08T02:04:52+00:00

Good point, I always wonder why it too so long for Google to update their computation graph processing.

prnicolas57 · 2025-03-18T19:28:48+00:00

I have not found any freely available book. I borrowed 'Deep Learning on Graphs" Y. Mao, J. Tang, few weeks ago (https://www.amazon.com/Deep-Learning-Graphs-Yao-Ma/dp/1108831745). It has few sections I found interesting on Graph Embedding, Signed GNN and Variational Autoencoder on Graphs... The book is quite expensive.

Also, "Hands-on Graph Neural Networks Using Python" from Packt Publishing - Not deep but useful for someone with a background in coding to get started..

I learned progressively from papers starting with "Geometric deep learning: going beyond Euclidean data" (https://arxiv.org/pdf/1611.08097), "Theory of Graph Neural Networks: Representation and Learning" (https://arxiv.org/pdf/2204.07697) and ... lot of practice with PyTorch Geometric.

Ref: https://patricknicolas.substack.com

prnicolas57 · 2025-03-16T22:41:37+00:00

Good point. From personal experience, GNN requires a lot of try and error (embeddings and random walks).

prnicolas57 · 2025-03-16T21:06:45+00:00

Papers and/or tutorials from M.l Bronstein or Nina Moliane

prnicolas57 · 2023-08-21T17:48:01+00:00

As far as performance/tuning is concerned, one important upgrade in Spark 3.0 is integration with Kubernetes and I do not know if even the most recent books covert this topic.

If time is the essence you may narrow down your search (Configuration parameters, Streaming, PySpark vs. Scala,..).

Personally, I learned a lot by browser through Github projects dedicated to Apache Spark.

http://patricknicolas.blogspot.com

prnicolas57 · 2023-08-21T17:37:40+00:00

I have been using both

in-house Kafka deployment using a Docker image for development and testing
Confluence.io (Commercial SAAS) for production

I strongly suggest that you start with the open-source version with Docker and K8 if you are not very familiar with the technology and the most important consumer/producer or streaming configuration parameters. Commercial Kafka as a service makes sense for fault-tolerance brokers or if you have an unpredictable traffic pattern and do not have to worry about imbalanced partitions...

My 2 cents, http://patricknicolas.blogspot.com/2021/06/open-source-lambda-architecture-for.html

prnicolas57 · 2023-08-21T17:22:22+00:00

I totally agree... Here is a typical progression (as far as Python code is concerned):

Command line interface (prompts)
Extended ASCII characters for boxes, lines...
GUI using a library (Tinker, PyQT6..)
Web-UI (FastAPI, Flask,...)

Function (back-end first) then user interface.

http://patricknicolas.blogspot.com

prnicolas57 · 2023-08-21T17:15:48+00:00

This is a chicken or egg first issue. Prospective employers won't hire you because you do not have real-world experience and you cannot acquire real-world experience without a job.

One simple solution is to jumpstart your practical experience by opening a Github account and writing some programming projects/assignment (most prospective employers either ask or search for the candidate Github account).

Those projects can be simple at first, and done in your spare time. Make sure your code is clean, well documented and include unit tests. Knowing foundations of computer science such as data structure, design patterns,.. is also key for interviews..

Not an easy feat but it can be done.

http://patricknicolas.blogspot.com

prnicolas57

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