Month 3 — Deep Learning

🎯 Goal of this month

By the end of the month you will be able to:

Understand what a neural network is and how it works
Build and train your own neural networks in PyTorch
Get familiar with TensorFlow/Keras
Do image classification with CNN
Process sequence data with RNN/LSTM
Apply transfer learning

Weekly breakdown

Week	Topic	Time
Week 1	Neural Network foundations + PyTorch	10-12 hours
Week 2	TensorFlow/Keras + Training techniques	10-12 hours
Week 3	CNN and Image Classification	10-12 hours
Week 4	RNN/LSTM + Transfer Learning	10-12 hours

Chapter order

Neural Network foundations — perceptron, backprop, intuition
PyTorch foundations — tensor, autograd, nn.Module
TensorFlow and Keras — alternative framework
Training techniques — optimizers, regularization, callbacks
CNN — Convolutional Networks — image classification
RNN, LSTM, GRU — sequence data
Exercises

What can you do by the end of the month?

Write nn.Module in PyTorch and build a training loop
Achieve 95%+ accuracy on datasets like MNIST, CIFAR-10
Fine-tune pretrained models (ResNet, EfficientNet)
GPU-powered prediction service via FastAPI
Ship ML models to production with torch.save / torch.jit

Tip for Backend Dev

DL = “Layered functions + Automatic differentiation”. You need two things:

Model architecture — stacking layers (like LEGO)
Training loop — for-each-batch: forward → loss → backward → optimizer

It looks complex at first, but after writing 2-3 examples you’ll feel the “pattern”.

About Hardware

DL is built not for CPU but for GPU. Options:

Mac M1/M2/M3 — MPS backend (PyTorch 2.0+) — enough for small models
Local NVIDIA GPU(RTX 3060+) — install CUDA + cuDNN
Google Colab — free T4 GPU (12 hours/session) — RECOMMENDED
Kaggle Notebooks — free P100 GPU (30 hours/week)
**Paid:**Lambda Labs, vast.ai, RunPod — $0.20-2 per hour

**Advice:**for local exercises CPU/M-chip, for capstone — Colab/Kaggle GPU.

Start

Go to Neural Network foundations.