PyTorch Mastery

From Tensors to Production — The Complete Deep Learning Framework Guide

Master every layer of PyTorch: tensor operations, automatic differentiation, neural network construction, training workflows, CNNs, RNNs, Transformers, transfer learning, and production deployment. Each part is standalone with independently executable code examples.

9Parts
5Architecture Deep Dives
9-Part Main Series

All Articles in This Series

A progressive learning path from PyTorch fundamentals through advanced architectures and production deployment. Each part builds on the previous while remaining independently valuable.

Part 1
Tensors, Autograd & GPU Fundamentals
35 min read
TensorsAutogradCUDA
Master PyTorch tensors, automatic differentiation, GPU acceleration, and the computational graph that powers all deep learning.
Read Article →
Part 2
Building Neural Networks
30 min read
nn.ModuleActivationsOptimizers
Build neural networks with nn.Module, understand activation functions, weight initialization, loss functions, and optimizers.
Read Article →
Part 3
Training, Evaluation & Checkpointing
30 min read
Training LoopMetricsMixed Precision
Master the training loop, model evaluation, saving/loading checkpoints, mixed-precision training, and distributed training strategies.
Read Article →
Part 4
Datasets, DataLoaders & Data Pipelines
25 min read
DatasetDataLoaderTransforms
Build custom datasets, efficient data loading pipelines, augmentation transforms, and handle real-world data challenges.
Read Article →
Part 5
CNNs & Computer Vision
35 min read
Conv2dPoolingClassification
Build convolutional neural networks from scratch, understand convolution math, pooling, and image classification pipelines.
Read Article →
Part 6
RNNs, LSTMs & Sequence Models
30 min read
RNNLSTMEmbeddings
Master recurrent networks, LSTM gates, GRUs, word embeddings, and sequence-to-sequence models for NLP and time series.
Read Article →
Part 7
Transformers & Attention Mechanisms
35 min read
AttentionTransformerViT
Implement scaled dot-product attention, multi-head attention, full Transformer encoder/decoder, and Vision Transformers in PyTorch.
Read Article →
Part 8
Transfer Learning & Pretrained Models
25 min read
Fine-tuningPretrainedtorchvision
Leverage pretrained models from torchvision and Hugging Face. Learn feature extraction, fine-tuning strategies, and domain adaptation.
Read Article →
Part 9
Deployment, Optimization & Best Practices
30 min read
TorchScriptONNXQuantization
Deploy models with TorchScript, ONNX export, quantization, mobile optimization, and production best practices.
Read Article →
5 Architecture Deep Dives

Architecture Deep Dives

Focused implementation guides for landmark neural network architectures. Each deep dive walks through the architecture paper, builds it from scratch in PyTorch, trains it on real data, and explains every design decision.

Deep Dive
ResNet: Residual Networks
35 min read
Skip ConnectionsImage Classification
Build ResNet-18 and ResNet-50 from scratch, understand the degradation problem, and train to >92% on CIFAR-10.
Read Deep Dive →
Deep Dive
U-Net: Image Segmentation
30 min read
Encoder-DecoderSegmentation
Implement U-Net’s encoder-decoder architecture with skip connections for pixel-perfect image segmentation.
Read Deep Dive →
Deep Dive
YOLO: Real-Time Object Detection
35 min read
Object DetectionReal-Time
Build YOLO from scratch — grid cells, bounding box regression, NMS, and modern YOLOv8 inference with Ultralytics.
Read Deep Dive →
Deep Dive
GANs: Generative Adversarial Networks
35 min read
GeneratorDiscriminator
Implement DCGAN and WGAN-GP from scratch, train on MNIST, and understand mode collapse and training stability.
Read Deep Dive →
Deep Dive
GPT-2 Mini: Text Generation
40 min read
AutoregressiveLanguage Model
Build GPT-2 from scratch — causal attention, transformer blocks, and train a character-level model on Shakespeare.
Read Deep Dive →