Deep Learning with PyTorch: Complete Mastery (2025 Edition)

From Neural Nets to Transformers — Production-Ready DL Goal: Build, train, and deploy state-of-the-art deep learning models using PyTorch — the #1 DL framework in research and industry.

Deep Learning with PyTorch: Complete Mastery (2025 Edition)

From Neural Nets to Transformers — Production-Ready DL

Goal: Build, train, and deploy state-of-the-art deep learning models using PyTorch — the #1 DL framework in research and industry.

Why PyTorch?
- Dominates AI Research: 80% of CVPR/ICML papers use PyTorch
- Dynamic Computation Graph: Debug like NumPy, scale like TensorFlow
- Production Ready: TorchServe, ONNX, PyTorch Lightning, Hugging Face
- Salary Impact: DL Engineers earn $150K–$300K+
- 2025 Trends: Multimodal, Efficient Training, Federated Learning

PyTorch Roadmap (6 Months, 2025)

Month	Focus	Key Skills	Resources
1	PyTorch Fundamentals	Tensors, Autograd, NN Module	Official Tutorial, DeepLearning.AI
2	CNNs & Computer Vision	ResNet, Transfer Learning, Segmentation	FastAI, Stanford CS231n
3	RNNs, LSTMs, Transformers	Seq2Seq, BERT, GPT	Hugging Face Course
4	Advanced Architectures	GANs, Diffusion, ViTs	Made With ML
5	MLOps & Deployment	TorchServe, ONNX, Lightning	PyTorch Lightning
6	Capstone: Custom Model from Scratch	Full pipeline + deployment	Kaggle, Hugging Face Spaces

PyTorch Core Concepts (Week 1–2)

1. Tensors — The Building Block

import torch

# Create tensors
x = torch.randn(3, 4)           # Random
y = torch.ones(3, 4, dtype=torch.float32)
z = torch.tensor([[1, 2], [3, 4]])

# Operations
x + y, x @ y.T, x.mean(), x.to('cuda')  # GPU!

2. Autograd — Automatic Differentiation

x = torch.tensor(2.0, requires_grad=True)
y = x ** 2 + 3 * x
y.backward()        # dy/dx = 2x + 3
print(x.grad)       # tensor(7.)

3. `nn.Module` — Your Model Blueprint

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

model = Net()

4. Training Loop

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch}, Loss: {loss.item():.4f}")

Computer Vision with CNNs (Month 2)

Build ResNet from Scratch

class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, stride, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        identity = x
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += identity
        return F.relu(out)

Transfer Learning (Fine-tuning)

from torchvision import models

model = models.resnet50(pretrained=True)
for param in model.parameters():
    param.requires_grad = False  # Freeze

# Replace final layer
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, num_classes)

# Train only classifier
optimizer = torch.optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)

Project:

Kaggle: Dogs vs Cats → 99.5% accuracy using efficientnet-b0 + TTA

NLP with Transformers (Month 3)

Load BERT with Hugging Face

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)

inputs = tokenizer("I love PyTorch!", return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits

Fine-tune on Custom Dataset

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    logging_dir='./logs',
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

trainer.train()

Project:

IMDB Sentiment → 94% accuracy with distilbert-base-uncased

Advanced Architectures (Month 4)

Architecture	Use Case	Key Paper
GANs	Image generation	Goodfellow et al., 2014
Diffusion Models	Stable Diffusion	Ho et al., 2020
Vision Transformers (ViT)	Image classification	Dosovitskiy et al., 2020
EfficientNet	Mobile efficiency	Tan & Le, 2019

Simple GAN

class Generator(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Linear(100, 256),
            nn.ReLU(),
            nn.Linear(256, 784),
            nn.Tanh()
        )

    def forward(self, z):
        return self.model(z).view(-1, 1, 28, 28)

# Train G and D alternately

MLOps & Deployment (Month 5)

PyTorch Lightning (Clean Training)

import pytorch_lightning as pl

class LitModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.model = Net()
        self.criterion = nn.CrossEntropyLoss()

    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self.model(x)
        loss = self.criterion(logits, y)
        self.log('train_loss', loss)
        return loss

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=1e-3)

trainer = pl.Trainer(max_epochs=10, gpus=1)
trainer.fit(model, train_loader)

Deploy with TorchServe

torch-model-archiver --model-name mnist \
  --version 1.0 \
  --serialized-file model.pth \
  --handler custom_handler.py

torchserve --start --model-store model_store --models mnist=mnist.mar

API Test:

curl http://localhost:8080/predictions/mnist -T image.jpg

Capstone: Build & Deploy Custom Model

Project: "Sign Language Recognition"

Dataset: Kaggle Sign Language MNIST
Model: CNN + Attention
Deployment: Hugging Face Space + Gradio UI

# app.py (Gradio)
import gradio as gr
import torch

model = torch.load("sign_language.pt")
model.eval()

def predict(image):
    tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        pred = model(tensor).argmax(1).item()
    return f"Sign: {chr(65 + pred)}"

interface = gr.Interface(fn=predict, inputs="image", outputs="text")
interface.launch()

Live Demo: huggingface.co/spaces/yourname/sign-lang

Portfolio Deliverables

Project	Tech	Link
MNIST CNN	PyTorch	GitHub
BERT Sentiment	Transformers	Colab
Stable Diffusion	Diffusers	Hugging Face
Sign Language App	Gradio + HF	Live

Interview Questions (Solve Live!)

Question	Your Answer
"What is Autograd?"	Reverse-mode diff, dynamic graph
"Why ReLU > Sigmoid?"	No vanishing gradient
"How does Attention work?"	Q, K, V → scaled dot-product
"BatchNorm vs LayerNorm?"	BN: per-batch, LN: per-sample
"Deploy PyTorch model?"	TorchScript → ONNX → Triton

Free Resources Summary

Resource	Link
PyTorch Official	pytorch.org/tutorials
Hugging Face NLP	huggingface.co/course
FastAI	course.fast.ai
PyTorch Lightning	pytorch-lightning.readthedocs.io
Stanford CS231n	cs231n.stanford.edu
Made With ML	madewithml.com

Pro Tips

Use torch.compile() (PyTorch 2.0+) → 2x speedup
Mixed Precision → torch.cuda.amp
Log with Weights & Biases → wandb.init()
Contribute to GitHub → fix bugs in torchvision
Write blogs → "How I fine-tuned BERT in 10 lines"

Final Checklist

Task	Done?
Train CNN from scratch	☐
Fine-tune BERT	☐
Build GAN	☐
Deploy with TorchServe	☐
Live Gradio app	☐

All Yes → You’re a Deep Learning Engineer!

Next: Advanced ML & MLOps

You can train models → now scale & monitor them.

Start Now:

pip install torch torchvision torchaudio
python -c "import torch; print(torch.cuda.is_available())"

Tag me when you deploy your first model!
You now build the future of AI.

Last updated: Nov 09, 2025

Deep Learning with PyTorch: Complete Mastery (2025 Edition)

From Neural Nets to Transformers — Production-Ready DL Goal: Build, train, and deploy state-of-the-art deep learning models using PyTorch — the #1 DL framework in research and industry.