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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PyTorch Self Learning\n\nThis structured self-learning journey into PyTorch focused on:\n\n* tensor intuition\n* mathematical understanding\n* computational thinking\n* multidimensional data manipulation\n* deep learning foundations\n\nThis repository is not intended to be:\n\n* an API dump\n* copy-paste tutorial collection\n* framework memorization guide\n\nInstead, the goal is to develop:\n\n\u003e operational intuition for tensor systems and computational mathematics.\n\n---\n\n# Philosophy\n\nMost PyTorch tutorials teach:\n\n```text\nFramework Syntax → Neural Networks → Hope For Intuition\n```\n\nThis repository follows a different path:\n\n```text\nTensor Intuition\n    ↓\nMathematical Operations\n    ↓\nComputational Structures\n    ↓\nDeep Learning Foundations\n```\n\nThe focus is on:\n\n* understanding tensor behavior\n* dimensional reasoning\n* reduction systems\n* computational graphs\n* mathematical structure\n\nbefore jumping into large models.\n\n---\n\n# Repository Goals\n\nThis project aims to build intuition for:\n\n* tensors\n* tensor geometry\n* tensor manipulation\n* automatic differentiation\n* multidimensional computation\n* linear algebra operations\n* reduction systems\n* computational graph reasoning\n\nusing progressively structured examples.\n\n---\n\n# Topics Covered So Far\n\n# 1. Hello PyTorch\n\nIntroduction to:\n\n* `torch`\n* tensors\n* tensor creation\n* tensor properties\n\nConcepts:\n\n* tensor shapes\n* datatypes\n* devices\n* multidimensional arrays\n\n---\n\n# 2. Tensor Creation\n\nCovered APIs:\n\n```python\ntorch.tensor()\ntorch.Tensor()\ntorch.zeros()\ntorch.ones()\ntorch.empty()\ntorch.rand()\ntorch.randn()\ntorch.randint()\ntorch.arange()\ntorch.linspace()\ntorch.logspace()\ntorch.eye()\ntorch.full()\ntorch.zeros_like()\ntorch.ones_like()\ntorch.rand_like()\ntorch.randn_like()\n```\n\nConcepts:\n\n* memory allocation\n* random initialization\n* tensor factories\n* Gaussian distributions\n* identity matrices\n\n---\n\n# 3. Tensor Information\n\nCovered APIs:\n\n```python\ntensor.shape\ntensor.size()\ntensor.dtype\ntensor.device\ntensor.ndim\ntensor.numel()\ntensor.requires_grad\n```\n\nConcepts:\n\n* tensor metadata\n* shape analysis\n* dimensionality\n* gradient tracking\n* device management\n\n---\n\n# 4. Tensor Type Conversion\n\nCovered APIs:\n\n```python\ntensor.float()\ntensor.double()\ntensor.int()\ntensor.long()\ntensor.bool()\ntensor.to()\ntensor.cpu()\ntensor.cuda()\n```\n\nConcepts:\n\n* datatype systems\n* precision\n* CPU/GPU movement\n* memory representation\n\n---\n\n# 5. Tensor Manipulation\n\nCovered APIs:\n\n```python\ntorch.cat()\ntorch.stack()\ntorch.split()\ntorch.chunk()\n\ntensor.view()\ntensor.reshape()\ntensor.permute()\ntensor.transpose()\ntensor.squeeze()\ntensor.unsqueeze()\ntensor.flatten()\ntensor.repeat()\ntensor.expand()\n```\n\nConcepts:\n\n* shape transformations\n* batching\n* dimension reordering\n* tensor replication\n* broadcasting intuition\n\n---\n\n# 6. Mathematical Operations\n\nCovered APIs:\n\n```python\ntorch.add()\ntorch.sub()\ntorch.mul()\ntorch.div()\ntorch.matmul()\ntorch.mm()\ntorch.bmm()\n\ntorch.exp()\ntorch.log()\ntorch.sqrt()\ntorch.pow()\ntorch.abs()\ntorch.sin()\ntorch.cos()\ntorch.tanh()\ntorch.relu()\ntorch.sigmoid()\n```\n\nConcepts:\n\n* element-wise operations\n* matrix multiplication\n* activation functions\n* exponential systems\n* tensor algebra\n\n---\n\n# 7. Reduction Operations\n\nCovered APIs:\n\n```python\ntorch.sum()\ntorch.mean()\ntorch.std()\ntorch.var()\ntorch.max()\ntorch.min()\ntorch.argmax()\ntorch.argmin()\ntorch.prod()\n```\n\nConcepts:\n\n* aggregation\n* statistical summaries\n* dimensional collapse\n* reduction reasoning\n\n---\n\n# 8. Comparison Operations\n\nCovered APIs:\n\n```python\ntorch.eq()\ntorch.ne()\ntorch.gt()\ntorch.lt()\ntorch.ge()\ntorch.le()\ntorch.where()\n```\n\nConcepts:\n\n* masking\n* conditional tensor selection\n* logical tensor systems\n* thresholding\n\n---\n\n# 9. Autograd\n\nCovered APIs:\n\n```python\ntensor.backward()\ntorch.autograd.grad()\ntorch.no_grad()\ntorch.enable_grad()\ntorch.set_grad_enabled()\n```\n\nConcepts:\n\n* computational graphs\n* automatic differentiation\n* chain rule\n* gradient propagation\n* backpropagation\n\n---\n\n# 10. Variable Operations\n\nCovered APIs:\n\n```python\ntensor.requires_grad_()\ntensor.detach()\ntensor.clone()\n```\n\nConcepts:\n\n* graph detachment\n* gradient control\n* memory copying\n* tensor tracking\n\n---\n\n# 11. Beginner Tensor Problems\n\nImplemented beginner-friendly problems involving:\n\n* tensor analysis\n* reductions\n* squeeze/unsqueeze\n* averaging\n* batching\n* indexing\n\nExample:\n\n* Student Marks Tensor Analysis\n\n---\n\n# 12. Intermediate Tensor Systems Problem\n\nImplemented:\n\n* separable Gaussian filtering\n* multidimensional tensor filtering\n* grouped convolutions\n* channel-wise operations\n\nConcepts:\n\n* tensor geometry\n* signal processing intuition\n* separable convolution\n* computational optimization\n\n---\n\n# Educational Design\n\nThe repository emphasizes:\n\n```text\nConcept\n    ↓\nMathematics\n    ↓\nTensor Operation\n    ↓\nAlgorithmic Thinking\n    ↓\nPyTorch Implementation\n```\n\nEach section typically includes:\n\n* explanations\n* mathematical formulas\n* algorithmic intuition\n* notebook exercises\n* standalone scripts\n\n---\n\n# Why This Repository Is Different\n\nThis project intentionally bridges:\n\n| Mathematics       | PyTorch           |\n| ----------------- | ----------------- |\n| Linear Algebra    | Tensor Operations |\n| Calculus          | Autograd          |\n| Geometry          | Tensor Shapes     |\n| Signal Processing | Convolutions      |\n| Optimization      | Gradient Systems  |\n\ninstead of treating PyTorch as merely a software library.\n\n---\n\n# Repository Structure\n\n```text\npytorch-self-learning/\n│\n├── basics/\n│   ├── tensor_creation/\n│   ├── tensor_information/\n│   ├── tensor_manipulation/\n│   ├── tensor_math/\n│   ├── reductions/\n│   ├── comparison_ops/\n│   ├── autograd/\n│   └── variables/\n│\n├── problems/\n│   ├── beginner/\n│   └── intermediate/\n│\n├── notebooks/\n│\n└── README.md\n```\n\n---\n# Intermediate Phase — Tensor Interaction Systems\n\n## Overview\n\nAfter learning how tensors represent images and perception systems in:\n\n```text id=\"g3l9dn\"\nplay_with_kalu\n```\n\nthe repository now transitions into a deeper layer of tensor understanding:\n\n```text id=\"0n8o3d\"\nTensor Interaction Systems\n```\n\nThis phase focuses on how tensors:\n\n* align\n* negotiate dimensions\n* route information\n* reorganize geometry\n* control visibility\n* interact structurally\n\nThe goal is to move beyond:\n\n```text id=\"e73z1f\"\nTensor as container\n```\n\ntoward:\n\n```text id=\"7yk4yl\"\nTensor as computational geometry system\n```\n\n---\n\n# Core Philosophy\n\nThis phase repeatedly emphasizes:\n\n```text id=\"8l3zci\"\nTensor dimensions are not merely sizes.\n\nThey carry structural meaning.\n```\n\nUnderstanding tensor interaction systems is foundational for:\n\n* CNNs\n* transformers\n* attention systems\n* latent representations\n* multimodal systems\n* scientific computing\n\n---\n\n# Directory Structure\n\n```text id=\"2afq4j\"\nintermediate/\n\n└── phase_02_tensor_interaction_systems/\n\n    ├── step_01_tensor_broadcasting.py\n    ├── step_02_broadcasting_geometry.py\n    ├── step_03_tensor_indexing.py\n    ├── step_04_boolean_masking.py\n    ├── step_05_gather_scatter.py\n    ├── step_06_tensor_memory_layout.py\n    ├── step_07_tensor_permutation_systems.py\n    └── step_08_tensor_alignment_systems.py\n```\n\n---\n\n# Learning Progression\n\nThis phase follows the progression:\n\n```text id=\"8g1ud0\"\nBroadcasting\n    ↓\nTensor Navigation\n    ↓\nInformation Visibility\n    ↓\nTensor Routing\n    ↓\nMemory Geometry\n    ↓\nDimension Reorganization\n    ↓\nRepresentation Alignment\n```\n\n---\n\n# Step-by-Step Learning Path\n\n---\n\n## Step 01 — Tensor Broadcasting\n\nCore intuition:\n\n```text id=\"jjlwm8\"\nDimensions negotiate compatibility.\n```\n\nTopics:\n\n* singleton dimensions\n* implicit expansion\n* broadcasting rules\n* shape compatibility\n* tensor interaction\n\nKey insight:\n\n```text id=\"ddlf6w\"\nBroadcasting is structured geometric alignment.\n```\n\n---\n\n## Step 02 — Broadcasting Geometry\n\nCore intuition:\n\n```text id=\"l6e6o4\"\nBroadcasting is tensor geometry negotiation.\n```\n\nTopics:\n\n* right-to-left alignment\n* dimensional compatibility\n* higher-dimensional broadcasting\n* geometric expansion\n* structural interaction\n\nKey insight:\n\n```text id=\"mjdb55\"\nTensor interaction depends on geometric compatibility.\n```\n\n---\n\n## Step 03 — Tensor Indexing\n\nCore intuition:\n\n```text id=\"uh54te\"\nIndexing is tensor navigation.\n```\n\nTopics:\n\n* tensor slicing\n* dimensional traversal\n* advanced indexing\n* boolean indexing\n* higher-dimensional indexing\n\nKey insight:\n\n```text id=\"y4br9n\"\nTensor indexing enables structured information access.\n```\n\n---\n\n## Step 04 — Boolean Masking\n\nCore intuition:\n\n```text id=\"a0zjlwm\"\nMasks control information flow.\n```\n\nTopics:\n\n* boolean tensors\n* logical masking\n* conditional selection\n* masked computation\n* sparse visibility\n\nKey insight:\n\n```text id=\"5rjlwm\"\nMasking enables selective tensor visibility.\n```\n\n---\n\n## Step 05 — Gather and Scatter\n\nCore intuition:\n\n```text id=\"zjlwm8\"\nTensors can dynamically route information.\n```\n\nTopics:\n\n* gather()\n* scatter()\n* tensor routing\n* sparse interaction\n* dynamic indexing\n\nKey insight:\n\n```text id=\"jqjlwm\"\nTensor systems can selectively redistribute information.\n```\n\n---\n\n## Step 06 — Tensor Memory Layout\n\nCore intuition:\n\n```text id=\"jlwmu5\"\nTensor geometry affects memory behavior.\n```\n\nTopics:\n\n* contiguous tensors\n* tensor storage\n* memory layout\n* tensor stride\n* reshape vs view\n\nKey insight:\n\n```text id=\"jlwmu6\"\nA tensor is simultaneously geometry and memory.\n```\n\n---\n\n## Step 07 — Tensor Permutation Systems\n\nCore intuition:\n\n```text id=\"jlwmu7\"\nDimension order changes tensor meaning.\n```\n\nTopics:\n\n* permute()\n* transpose()\n* dimension reordering\n* semantic structure\n* tensor reinterpretation\n\nKey insight:\n\n```text id=\"jlwmu8\"\nTensor dimensions encode semantic structure.\n```\n\n---\n\n## Step 08 — Tensor Alignment Systems\n\nCore intuition:\n\n```text id=\"jlwmu9\"\nDeep learning is structured tensor interaction.\n```\n\nTopics:\n\n* tensor alignment\n* representation compatibility\n* interaction geometry\n* embedding alignment\n* attention alignment\n\nKey insight:\n\n```text id=\"jlwmua\"\nTensor systems interact through structural negotiation.\n```\n\n---\n\n# Important Conceptual Shift\n\nPhase 1 taught:\n\n```text id=\"jlwmub\"\nHow tensors represent perception.\n```\n\nPhase 2 teaches:\n\n```text id=\"jlwmuc\"\nHow tensors structurally interact.\n```\n\nThis transition is extremely important because modern AI systems fundamentally depend on:\n\n* tensor alignment\n* tensor routing\n* dimension organization\n* latent interaction systems\n\n---\n\n# Why This Phase Matters\n\nMost tutorials stop at:\n\n* tensor creation\n* tensor arithmetic\n* basic neural networks\n\nThis repository intentionally explores:\n\n```text id=\"jlwmud\"\nThe computational geometry of tensor systems.\n```\n\nThese ideas form the foundation for understanding:\n\n* transformers\n* attention\n* embeddings\n* multimodal systems\n* latent spaces\n* representation learning\n\n---\n\n# Important Deep Learning Connection\n\nNearly every modern AI architecture depends heavily on:\n\n* broadcasting\n* masking\n* permutation\n* alignment\n* tensor routing\n\nUnderstanding these deeply transforms how one understands AI systems.\n\n---\n\n# Final Conceptual Summary\n\nThis phase gradually reveals that deep learning systems are fundamentally:\n\n```text id=\"jlwmue\"\nlarge-scale tensor interaction systems\n```\n\nwhere dimensions carry:\n\n* structure\n* semantics\n* geometry\n* interaction meaning\n\nrather than merely:\n\n```text id=\"jlwmuf\"\nnumerical storage.\n```\n\n\n# Future Topics\n\nPlanned future sections include:\n\n* convolution operations\n* neural networks\n* CNNs\n* transformers\n* attention mechanisms\n* optimization\n* CUDA programming\n* custom autograd\n* latent representations\n* tensor geometry\n* diffusion systems\n\n---\n\n# Recommended Audience\n\nThis repository is especially useful for:\n\n* self learners\n* engineers transitioning from MATLAB\n* scientific computing learners\n* ML beginners wanting intuition\n* researchers building tensor understanding\n\n---\n\n# Final Note\n\nPyTorch is more than a deep learning framework.\n\nIt is fundamentally:\n\n\u003e a multidimensional computational mathematics system.\n\nThe goal of this repository is to learn PyTorch not merely as:\n\n* syntax\n\nbut as:\n\n* tensor reasoning\n* computational structure\n* mathematical abstraction\n* differentiable systems engineering.\n \n\n# How To Use\n\n## 1. Clone Repository\n\n```bash id=\"h1\"\ngit clone git@github.com:kncsolutions/pytorch-self-learning.git\n```\n\nEnter project directory:\n\n```bash id=\"h2\"\ncd pytorch-self-learning\n```\n\n---\n\n# 2. Create Virtual Environment (Recommended)\n\n## Using `venv`\n\n```bash id=\"h3\"\npython -m venv venv\n```\n\nActivate environment:\n\n### Linux / macOS\n\n```bash id=\"h4\"\nsource venv/bin/activate\n```\n\n### Windows\n\n```bash id=\"h5\"\nvenv\\Scripts\\activate\n```\n\n---\n\n# 3. Install Dependencies\n\nInstall PyTorch.\n\nVisit:\n\n```text id=\"h6\"\nhttps://pytorch.org/get-started/locally/\n```\n\nor install CPU version directly:\n\n```bash id=\"h7\"\npip install torch torchvision torchaudio\n```\n\nInstall notebook support:\n\n```bash id=\"h8\"\npip install notebook matplotlib\n```\n\n---\n\n# 4. Run Python Scripts\n\nExample:\n\n```bash id=\"h9\"\npython basics/tensor_creation.py\n```\n\nRun mathematical operations:\n\n```bash id=\"h10\"\npython basics/tensor_mathematics.py\n```\n\nRun tensor manipulation examples:\n\n```bash id=\"h11\"\npython basics/tensor_manipulation.py\n```\n\n---\n\n# 5. Launch Jupyter Notebook\n\nStart notebook server:\n\n```bash id=\"h12\"\njupyter notebook\n```\n\nOpen notebook examples from:\n\n```text id=\"h13\"\nnotebooks/\n```\n\nExample notebooks:\n\n```text id=\"h14\"\nnotebooks/basics/tensor_creation.ipynb\n\nnotebooks/basics/tensor_mathematics.ipynb\n\nnotebooks/basics/tensor_manipulation.ipynb\n```\n\n---\n\n# 6. Solve Beginner Problems\n\nProblem files are available in:\n\n```text id=\"h15\"\nproblems/beginner/\n```\n\nExample:\n\n```bash id=\"h16\"\npython problems/beginner/tensor_basic_task_easy.py\n```\n\n---\n\n# 7. Check Solutions\n\nSolutions are available in:\n\n```text id=\"h17\"\nsolutions/\n```\n\nExample:\n\n```bash id=\"h18\"\npython solutions/tensor_basic_task_easy_solution.py\n```\n\n---\n\n# 8. Intermediate Demonstrations\n\nIntermediate tensor systems demonstrations are available in:\n\n```text \nproblems/intermediate/\n```\n\nExample:\n\n```\npython problems/intermediate/tensor_basic_demonstration_advanced.py\n```\n\n---\n\n# Recommended Learning Flow\n\nRecommended progression:\n\n```text id=\"h21\"\n1. Read notebook explanation\n        ↓\n2. Run standalone script\n        ↓\n3. Modify tensor values\n        ↓\n4. Observe tensor behavior\n        ↓\n5. Solve beginner problem\n        ↓\n6. Compare with solution\n        ↓\n7. Experiment independently\n```\n\n---\n\n# Suggested Execution Order\n\n## Basics\n\n```text id=\"h22\"\n1. hello_pytorch.py\n2. tensor_creation.py\n3. tensor_information.py\n4. tensor_type_conversion.py\n5. tensor_manipulation.py\n6. tensor_mathematics.py\n7. tensor_reduction.py\n8. tensor_comparison.py\n9. tensor_autograd_playground.py\n10. tensor_variable_operations.py\n```\n\n---\n\n# Important Recommendation\n\nDo NOT merely read the code.\n\nTry:\n\n* changing tensor shapes\n* modifying dimensions\n* changing datatypes\n* experimenting with reductions\n* intentionally creating errors\n\nTensor intuition develops through experimentation.\n\n---\n\n# Development Environment\n\nCurrent project structure is compatible with:\n\n* Jupyter Notebook\n* VSCode\n* PyCharm\n* Spyder\n* Linux terminal workflows\n\n---\n````markdown id=\"wqlycg\"\n## Installation\n\n### CPU Installation\n\n```bash\npip install torch torchvision --index-url https://download.pytorch.org/whl/cpu\n````\n\n### Install Additional Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n```\n```\n\n# Verify Installation\n\nRun:\n\n```\npython\n```\n\nThen:\n\n```\nimport torch\n\nprint(torch.__version__)\n```\n\nIf PyTorch imports successfully, setup is complete.\n\n# Developed By\n\nDeveloped by:\n\n```text id=\"d1\"\nPallav Nandi Chaudhuri\n```\n\nThis repository is part of a structured self-learning and research-oriented exploration into:\n\n* PyTorch\n* tensor systems\n* computational mathematics\n* deep learning foundations\n* multidimensional computation\n* differentiable systems\n\n---\n\n# Contact / Further Guidance\n\nFor:\n\n* questions\n* suggestions\n* collaborations\n* corrections\n* learning discussions\n* research-oriented conversations\n\nfeel free to connect through the repository discussions/issues section.\n\nIf this repository helps your learning journey, consider:\n\n* starring the repository\n* contributing improvements\n* extending exercises\n* experimenting independently\n\n---\n\n# Final Note\n\nThe objective of this repository is not merely:\n\n* learning APIs\n\nbut developing:\n\n* tensor intuition\n* computational reasoning\n* multidimensional thinking\n* mathematical understanding of deep learning systems.\n\nLearning PyTorch deeply means learning:\n\n\u003e how modern AI systems manipulate structured numerical geometry.\n\u003e \n\u003e # License\n\nThis project is licensed under the MIT License.\n\nSee:\n- LICENSE\n- DISCLAIMER.md\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkncsolutions%2Fpytorch-self-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkncsolutions%2Fpytorch-self-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkncsolutions%2Fpytorch-self-learning/lists"}