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Training Machine Algorithms
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Training Machine Algorithms
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abhaskumarsinha/README.md

👋 Welcome to My GitHub Profile

Hey there! I'm Abhas (aka R3wC13), a student who's always exploring the realms of coding and machine learning.

I talk about Philosophy, Physics, Computation, and AI. If you have something that could interest me, feel free to drop a few of your reading lists to me. :-)

Given today, the tremendous usage of computation systems that consume a lot of power to train large AI systems, it is my goal to realize quantum computational methods (or at least hybrid systems) in the next few decades of future, hence providing a piece of the puzzle solving computational problems for AGI realization.

Skills and Interests

  • Programming Languages: Python, C, Assembly (preferably NASM), PHP, SQL.
  • Tools and Frameworks: TensorFlow, PyTorch, Sci-kit Learn.
  • Philosophy: Shunya (Madhyamaka Buddhism), Advaita Vedanta (Ashtavakra's), Sankhya (Kapila's).
  • Field: AI (Supervised, Unsupervised Learning, Alignment, Domain Adaptation and related Areas, GPT and text based models), Physics (Quantum Mechanics, Field Theory, General Relativity), Mathematics (Number Theory, Statistics).

Your GitHub Stats

Some of the Quotes in Philosophical formulations


If, indeed, the present and future are contingently related
  to the past, they should exist in the past moment.

If, again, the present and future do not exist there (i.e.,
   in the past), how could they be contingently related?

Again, it is not possible for both (present and future) to
   establish themselves without being contingent on a past.
   Therefore, there is no justification for the existence of a
   present and a future time.

          - Inada, Kenneth K. Nāgārjuna: A translation of his Mūlamadhyamakakārikā with an introductory essay. Vol. 127. Sri Satguru Publications, 1993.

The contradiction arises because if the present and future depend on the past, they either pre-exist (negating their distinctness) or lack a basis for dependence (making the linear-time concept meaningless). Nagarjuna exposes this to challenge our attachment to fixed time concepts and urges us towards understanding emptiness, the absence of inherent existence in phenomena like time in Madhyamaka Buddhism.


2.4
As waves, foam, and bubbles
are not different from water,
so the universe emanating from Self
is not different from Self.

2.5
Look closely at cloth,
you see only threads.
Look closely at creation,
you see only Self.
          - Ashtavakra Gita
In these verses from the Ashtavakra Gita, the speaker employs metaphors to convey the Advaita Vedanta principle of non-duality. The analogy of waves, foam, and bubbles being inseparable from water illustrates the oneness of the universe with the Self (Atman/Brahman). Similarly, the comparison of creation to cloth and threads emphasizes that, upon close examination, diversity in the world dissolves into the singular essence of the Self. The verses underscore the non-dual nature of reality, urging contemplation on the unity underlying the apparent multiplicity of the universe, like a dream collapsing into a person.

The world is experienced by our consciousness,
Is the world which is experienced by our consciousness something separate from the experiencing consciousness?
if it is, if you claim that the world is separate from the experiencing consciousness,
prove it!
You cannot prove it, because to prove it, you'll have to experience the world apart from consciousness.
But nothing can be experienced apart from consciousness!

Just as the table and wood cannot be separated, the "table" is the name and form of the "wood",
Universe is the name and form of the consciousness!
          - Swami Sarvapriyananda
These verses lead to the formulation of Advaita Vedanta philosophy, where the whole universe is nothing, but an appearance of consciousness. Self (or consciousness) is the real cause of the universe, as in the previous vereses in Ashtavakra Gita tell about.

1. (1)
Neither from itself nor from another
Nor from both,
Nor without a cause
Is anything ever generated anywhere.
          - MoolmadhyamakaKarika. Nagajurna.(1)I.
On the creation of the universe - either the universe manifested (or came into existence) by itself, or from a God, or both, or a cause. If the universe that is unexisting initially can come into existence from something or itself, or both, or with a cause, then how there was non-existence before it? (as either itself, God, both, or cause must have existed). If something did exist before it, then how come it is a manifestation of existence from non-existence? This proves that non-existence is itself self-contradictory for the universe to have created, hence there is no harm in assuming universe (or existence) eternally existed.

Thanks for Coming!

Thanks for dropping by! Connect with me on LinkedIn to stay in touch.

LinkedIn | HuggingFace

Pinned

  1. Corpus2GPT Corpus2GPT Public

    Corpus2GPT: A project enabling users to train their own GPT models on diverse datasets, including local languages and various corpus types, using Keras and compatible with TensorFlow, PyTorch, or J…

    Jupyter Notebook 4

  2. Keras-implementation-of-Transformer-Architecture Keras-implementation-of-Transformer-Architecture Public

    This repository presents a Python-based implementation of the Transformer architecture on Keras TensorFlow library, as proposed by Vaswani et al. in their 2017 paper "Attention is all you need."

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  3. Seq2Seq-Bahdanau-Attention-based-Encoder-Decoder-Language-Translator Seq2Seq-Bahdanau-Attention-based-Encoder-Decoder-Language-Translator Public

    A language translator based on a very simple NLP Transformer model, backed by encoder, decoder and a Bahdanau Attention Layer in between, implemented on TensorFlow.

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  4. GRU-varients GRU-varients Public

    An implementation of classical GRU (Cho, el at. 2014) along with Optimized versions (Dey, Rahul. 2017) on TensorFlow that outperforms Native tf.keras.layers.GRU(units) implementation of Keras.

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    A very simple and plain DC GAN to generate Image Flower Pictures out of the dataset.

    Jupyter Notebook 2

  6. MinimalGPT MinimalGPT Public

    MinimalGPT is a concise, adaptable, and streamlined code framework that encompasses the essential components necessary for the construction, training, inference, and fine-tuning of the GPT model. T…

    Python 19 6