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Welcome to the next part of our Deep Learning with Python, TensorFlow, and Keras tutorial series. In this tutorial, we're going to continue building our cryptocurrency-price-predicting Recurrent Neural Network.

Text tutorials and sample code: https://pythonprogramming.net/....balancing-rnn-data-d

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"Symmetry, as wide or narrow as you may define its meaning, is one idea by which man through the ages has tried to comprehend and create order, beauty, and perfection." and that was a quote from Hermann Weyl, a German mathematician who was born in the late 19th century.

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation.

While learning generic functions in high dimensions is a cursed estimation problem, many tasks are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world.

Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr.
Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Enjoy the show!

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://arxiv.org/abs/2104.13478

[00:00:00] Tim Intro
[00:01:55] Fabian Fuchs article
[00:04:05] High dimensional learning and curse
[00:05:33] Inductive priors
[00:07:55] The proto book
[00:09:37] The domains of geometric deep learning
[00:10:03] Symmetries
[00:12:03] The blueprint
[00:13:30] NNs don't deal with network structure (TedX)
[00:14:26] Penrose - standing edition
[00:15:29] Past decade revolution (ICLR)
[00:16:34] Talking about the blueprint
[00:17:11] Interpolated nature of DL / intelligence
[00:21:29] Going tack to Euclid
[00:22:42] Erlangen program
[00:24:56] “How is geometric deep learning going to have an impact”
[00:26:36] Introduce Michael and Petar
[00:28:35] Petar Intro
[00:32:52] Algorithmic reasoning
[00:36:16] Thinking fast and slow (Petar)
[00:38:12] Taco Intro
[00:46:52] Deep learning is the craze now (Petar)
[00:48:38] On convolutions (Taco)
[00:53:17] Joan Bruna's voyage into geometric deep learning
[00:56:51] What is your most passionately held belief about machine learning? (Bronstein)
[00:57:57] Is the function approximation theorem still useful? (Bruna)
[01:11:52] Could an NN learn a sorting algorithm efficiently (Bruna)
[01:17:08] Curse of dimensionality / manifold hypothesis (Bronstein)
[01:25:17] Will we ever understand approximation of deep neural networks (Bruna)
[01:29:01] Can NNs extrapolate outside of the training data? (Bruna)
[01:31:21] What areas of math are needed for geometric deep learning? (Bruna)
[01:32:18] Graphs are really useful for representing most natural data (Petar)
[01:35:09] What was your biggest aha moment early (Bronstein)
[01:39:04] What gets you most excited? (Bronstein)
[01:39:46] Main show kick off + Conservation laws
[01:49:10] Graphs are king
[01:52:44] Vector spaces vs discrete
[02:00:08] Does language have a geometry? Which domains can geometry not be applied? +Category theory
[02:04:21] Abstract categories in language from graph learning
[02:07:10] Reasoning and extrapolation in knowledge graphs
[02:15:36] Transformers are graph neural networks?
[02:21:31] Tim never liked positional embeddings
[02:24:13] Is the case for invariance overblown? Could they actually be harmful?
[02:31:24] Why is geometry a good prior?
[02:34:28] Augmentations vs architecture and on learning approximate invariance
[02:37:04] Data augmentation vs symmetries (Taco)
[02:40:37] Could symmetries be harmful (Taco)
[02:47:43] Discovering group structure (from Yannic)
[02:49:36] Are fractals a good analogy for physical reality?
[02:52:50] Is physical reality high dimensional or not?
[02:54:30] Heuristics which deal with permutation blowups in GNNs
[02:59:46] Practical blueprint of building a geometric network architecture
[03:01:50] Symmetry discovering procedures
[03:04:05] How could real world data scientists benefit from geometric DL?
[03:07:17] Most important problem to solve in message passing in GNNs
[03:09:09] Better RL sample efficiency as a result of geometric DL (XLVIN paper)
[03:14:02] Geometric DL helping latent graph learning
[03:17:07] On intelligence
[03:23:52] Convolutions on irregular objects (Taco)

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Understand the BERT Transformer in and out.

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REFERENCES

[1] BERT main paper: https://arxiv.org/pdf/1810.04805.pdf
[1] BERT in google search: https://blog.google/products/s....earch/search-languag
[2] Overview of BERT: https://arxiv.org/pdf/2002.12327v1.pdf
[4] BERT word embeddings explained: https://medium.com/@_init_/why....-bert-has-3-embeddin
[5] More details of BERT in this amazing blog: https://towardsdatascience.com..../bert-explained-stat
[6] Stanford lecture slides on BERT: https://nlp.stanford.edu/semin....ar/details/jdevlin.p

Meta AI’s recently shared Open Pretrained Transformer (OPT-175B), a language model with 175 billion parameters trained on publicly available data sets.

For the first time for a language technology system of this size, the release includes both the pretrained models and the code needed to train and use them.

This video contains three parts:

00:00 Quick Intro about OPT-175B
03:51 OPT-175B Live Demo with Alpa
08:07 OPT1.3B Text Generation Hands-on Coding using Hugging Face Transformers

Colab OPT1.3B Text Generation Tutorial - https://colab.research.google.....com/drive/1ZGfFzL9au

OPT-175B Blog post from Meta AI - https://ai.facebook.com/blog/d....emocratizing-access-

OPT-175B Live Demo - https://opt.alpa.ai/

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This Edureka's Microservices video gives you detail of Microservices Architecture and how it is different from Monolithic Architecture. You will understand the concepts using a UBER case study. In this video, you will learn the following:
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00:00:14 Agenda
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00:02:16 Monolithic Architecture - Challenges
00:05:30 Microservices - Communication
00:06:46 Monolithic vs Microservice Architecture
00:07:10 Microservices Features
00:07:50 Use-Case - Uber
00:08:00 Uber's Previous System
00:09:05 Expansion Of Uber
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00:09:31 Uber Changed It's Architecture
00:11:31 Microservices Architecture: Best Practices
00:11:53 Session In A Minute

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