10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models • Deep Learning & PyTorch Neural Networks Architecture • Rishabh Ki Kaksha

Deep Learning & PyTorch Neural Networks Architecture › 10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models

45 mins

← Prev Next →

Course Syllabus

Module 1: PyTorch Tensors, Autograd & Computational Graphs

01. PyTorch Tensor Architecture: Strides, Storage Buffers, Memory Layout (Contiguous vs Non-Contiguous) 02. Dynamic Computational Graphs & The PyTorch Autograd Engine (grad_fn, requires_grad, backward) 03. GPU Acceleration with CUDA: Tensor Device Placement (to("cuda")), CUDA Streams & Pinned Memory

Module 2: Multi-Layer Perceptrons (MLPs) & Custom Training Loops

04. Building Custom nn.Module Classes, Custom Parameters & Activation Functions (ReLU, GELU, Swish) 05. Custom Datasets & High-Performance DataLoaders (num_workers, collate_fn, pin_memory) 06. The Enterprise Training Loop: Forward Pass, Loss Computation, Zero Grad, Backward Pass & Optimizer Step 07. Neural Regularization Techniques: Dropout, Batch Normalization (BatchNorm1d) & Layer Normalization (LayerNorm)

Module 3: Convolutional Neural Networks (CNNs) & Computer Vision Architecture

08. Convolutional Operations: Kernels, Filters, Strides, Padding & Feature Map Dilation 09. Modern CNN Architectures: Residual Connections (ResNet), Squeeze-and-Excitation & ConvNeXt 10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models

Module 4: Recurrent Architectures: RNNs, LSTMs & GRUs for Sequential Data

11. Sequential Modeling: Vanilla RNNs & The Vanishing / Exploding Gradient Problem 12. Long Short-Term Memory (LSTM) Networks: Forget Gate, Input Gate, Candidate State & Output Gate Math 13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch

Module 5: Modern Transformer Architecture from Scratch in PyTorch

14. Scaled Dot-Product Attention: Query, Key, Value Matrices & Attention Softmax Calculation 15. Multi-Head Attention (MHA) & Causal Masking Implementation from Scratch in Pure PyTorch 16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch

Module 6: PyTorch Distributed (DDP), Mixed Precision (AMP) & Model Checkpointing

17. Automatic Mixed Precision (torch.cuda.amp.autocast & GradScaler) for 2x Faster GPU Training 18. Distributed Data Parallel (DDP) Architecture: Multi-GPU Synchronization with torch.distributed 19. Robust Checkpointing: Saving State Dicts, Resuming Training & Early Stopping Callbacks 20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation

Interactive Lesson Studio

# 10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models

> Deep Learning & PyTorch Notes by Rishabh - Topic 10

---

### 1. Transfer Learning Strategies 🔄

1. **Feature Extraction (Frozen Backbone):** Backbone weights ko freeze karo (`param.requires_grad = False`), sirf final Linear classification head train karo.
2. **Full Fine-Tuning (Differential Learning Rates):** Backbone ko very low learning rate ($10^{-5}$) aur naye classifier head ko standard learning rate ($10^{-3}$) se train karo.

---

### 2. PyTorch Transfer Learning Implementation

```python
import torchvision.models as models
import torch.nn as nn

# Load pre-trained ResNet-50 on ImageNet-1K:
model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)

# Freeze backbone layers:
for param in model.parameters():
    param.requires_grad = False

# Replace final 1000-class classifier with custom 10-class head:
num_features = model.fc.in_features
model.fc = nn.Sequential(
    nn.Linear(num_features, 256),
    nn.ReLU(),
    nn.Dropout(0.3),
    nn.Linear(256, 10) # Custom 10 classes
)
```

---

### 3. Interview Ke 3 Sawal Is Topic Se

1. **Pre-trained models me ImageNet normalization values (`mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]`) kyu zaroori hoti hain?** -> Model weights in specific distribution statistics par train huye the; un-normalized images pass karne par feature activation distributions distort ho jaati hain.
2. **Discriminative Layer Training (Layer-wise LR Decay) kya hota hai?** -> Early layers (edges/textures) par minimum learning rate aur deeper semantic layers par progressively higher learning rate apply karna.
3. **Data Augmentation techniques (MixUp / CutMix) generalization kaise improve karti hain?** -> Virtual training images interpolate karke model ko linear feature space boundaries learn karne par force karti hain.

---
**Next Topic:** 11. Sequential Modeling: Vanilla RNNs & The Vanishing / Exploding Gradient Problem

Loading lesson content...

Python 3.12

Live Execution Terminal

Click "Execute Live" to run code.
                