Muhammad Allah Rakha (@aaaastark)
github

aaaastark

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Top repositories

1

Data-Scientist-Books

Data-Scientist-Books (Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Long Short Term Memory, Generative Adversarial Network, Time Series Forecasting, Probability and Statistics, and more.)
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2

Goal-Kicker-Notes-Professional-Programming-Languages

Goal-Kicker-Notes-Professional-Programming-Languages (goal kicker)
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3

Intrusion-Detection-System

Attack Detection, Parameter Optimization and Performance Analysis in Enterprise Networks (ML Networks) for Intrusion Detection System IDS.
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4

False-Data-Injection-Attack

False Data Injection Attack (FDIA) with Long Sort Term Memory (LSTM) Model using Python
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5

Top-Big-Data-Scientist-Questions-For-Interview

Top Big Tech Data Science Questions
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6

Pretrain_Finetune_Transformers_Pytorch

Pre-Training and Fine-Tuning transformer models using PyTorch and the Hugging Face Transformers library. Whether you're delving into pre-training with custom datasets or fine-tuning for specific classification tasks, these notebooks offer explanations and code for implementation.
Jupyter Notebook
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7

adversarial-network-attack-noise-on-mnist-dataset-pytorch

Adversarial Network Attacks (PGD, pixel, FGSM) Noise on MNIST Images Dataset using Python (Pytorch)
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8

Human_Resource_Management_System

Website to Human Resource Management System of the Employee Dashboard.
CSS
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9

NVIDIA-Speech-Artificial-Intelligence

NVIDIA Speech Artificial Intelligence. Speech AI Summit 2022.
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10

hybrid-model-with-cnn-lstm-python

Hybrid Model with CNN and LSTM for VMD dataset using Python
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11

Deep-Learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog. The adjective "deep" in deep learning comes from the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, and then that a network with a nonpolynomial activation function with one hidden layer of unbounded width can on the other hand so be. Deep learning is a modern variation which is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability, whence the "structured" part.
Python
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12

aaaastark

aaaastark
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13

Muhammad_Allah_Rakha_CV

Resume @aaaastark
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14

15-Programming-Laguage

Fifteen programming language are written in One Book form format. Such as the language is (Python, Ruby, PHP, Perl, Rust, R, Julia, Lua, Swift, C, C++, C#, Java, JavaScript, Go)
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15

Hyperspectral_Image_Denoising_AAFEHDN

Hyperspectral Image Denoising using Attention and Adjacent Features Extraction Hybrid Dense Network
Jupyter Notebook
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16

Natural-Language-Processing

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that studies how machines understand human language.Natural Language Processing (NLP) makes it possible for computers to understand the human language. Probably, the most popular examples of NLP in action are virtual assistants, like Google Assist, Siri, and Alexa. NLP understands and translates the human language, like “Hey Siri, where is the nearest gas station?” into numbers, making it easy for machines to understand.
Python
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17

Accelerometer-Sensors-Analysis

Time Series Analysis: Accelerometer Sensors of Object Inclination and Vibration. Time Series Analysis by using different (State of Art Models) Machine and Deep Learning. Recurent Neural Network with CuDNNLSTM Model, Convolutional Autoencoder, Residual Network (ResNet) and MobileNet Model.
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18

NeMo-WeightsBiases-TTS

Training and Tunning a Text to speech model with Nvidia NeMo and Weights and Biases
Jupyter Notebook
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