Freaks City

Version Checking # Check CUDA version (Command Prompt) nvcc -V or nvcc --version # Check Python version (Command Prompt) python # Check available CUDA versions (Command Prompt) nvidia-smi # CUDA Version is displayed after this text Installation Process 1. Visual Studio Installation Version Selection: For CUDA 11.8.0 (can be higher th ...

Convolution is a foundational operation in deep learning—especially in computer vision—where it enables hierarchical feature extraction through localized, parameter-shared transformations. Unlike general matrix multiplication, convolution exploits spatial locality and translation invariance, making it both computationally efficient and semantic ...

Data Preparation The dataset originates from a Kaggle competition involving store item demand forecasting. It contains 5 years of sales data (2013-2017) for 50 items across 10 stores, requiring predictions for the next 3 months (January-March 2018). This represents a multi-step multivariate time series problem with 500 distinct time series to f ...

PyTorch with CUDA 11.8 To install PyTorch 2.2.0 with CUDA 11.8 support: pip install torch==2.2.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 PyTorch with CUDA 12.4 For CUDA 12.4 compatibility, use: pip install torch==2.4.0+cu124 --extra-index-url https://download.pytorch.org/whl/cu124 LMdeploy Minimum Requirements LMdeploy ...

The Fundamental Concept of Generalization Generalization represents one of the most critical concepts in machine learning. It describes how well a model trained on specific datasets performs when encountering previously unseen data. In machine learning workflows, we utilize training datasets to develop models that establish mapping relationship ...

Attention Mechanisms and Transformers The attention mechanism addresses a fundamental challenge in deep learning: transforming variable-dimensional inputs into fixed-dimensional outputs through a weighted aggregation process. This capability proves essential when dealing with sequences or sets of varying sizes, where traditional fixed-parameter ...

Nearest Neighbor and k-NN Classifiers The Nearest Neighbor classifier stores the entire training set and predicts labels by finding the closest training example using a distance metric like L1 (Manhattan) or L2 (Euclidean). While simple, it suffers from high prediction latency (O(n)) and large memory usage. class KNearestNeighbor: def init(sel ...

Task Overview Sentiment classification is a fundamantal task in Natural Language Processing (NLP) that involves categorizing text (such as reviews or tweets) based on emotional sentiment (e.g., binary classification: positive/negative). In this tutorial, we'll use the IMDB movie review dataset to implement three different models using PyTorch. ...

Automatic differentiation (AD) is a fundamental technique in deep learning frameworks. This article demonstrates how to implement forward-mode automatic differentiation using Python operator overloading. The implemantation is remarkably concise—you can build a working system in just a few lines of code. Understanding Forward-Mode Automatic Diff ...

Automatic differentiation (AD) computes exact derivatives efficiently by applying the chain rule during program execution. Two primary strategies exist: forward mode and reverse mode. Their suitability depends on the shape of the function being differentiated—specifically, the number of inputs versus outputs. Intuitive Analogy: Manufacturing Wo ...