Last Updated on August 15, 2025

Deep Learning Essentials — Zero to Hero Series

📅 By Pranu Kumar
💡 Master ANN, CNN, and RNN from scratch to production-ready deployment

Series Overview

Deep Learning is the backbone of modern AI — powering everything from image recognition to natural language processing.
This Zero to Hero series will take you step-by-step from the basics of Artificial Neural Networks (ANN) to Convolutional Neural Networks (CNN) for computer vision and Recurrent Neural Networks (RNN) for sequence modeling.

What you’ll learn:

  • Fundamentals of deep learning & PyTorch
  • Building and training ANN for classification & regression
  • Designing CNN architectures for image datasets
  • Applying RNN/LSTM/GRU for text & time series data
  • Best practices for optimization, regularization & deployment

Modules

Module 0 — Foundations of Deep Learning

Before diving into neural networks, you’ll master:

  • Tensors, autograd, and computational graphs
  • Dataset preparation & train-validation-test splits
  • Loss functions, metrics, optimizers
  • Overfitting, underfitting, and bias–variance tradeoff

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Module 1 — Artificial Neural Networks (ANN)

Learn how to build your first neural network:

  • Perceptron & multi-layer perceptron (MLP)
  • Activation functions, initialization, dropout
  • ANN for classification (Iris dataset)
  • ANN for regression with regularization

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Module 2 — Advanced ANN Techniques

Go deeper into improving ANN performance:

  • Batch normalization & Layer normalization
  • Early stopping, learning rate schedules
  • Embedding categorical features
  • Model checkpointing & reproducibility

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Module 3 — Convolutional Neural Networks (CNN)

Unlock the power of deep learning for images:

  • Convolution operation, kernels, pooling
  • Feature hierarchy & receptive field
  • Designing CNN for MNIST & CIFAR-10
  • Residual connections & modern CNN blocks

📖 Read Full Article → (Embed Article 3 link)

Module 4 — CNN in Practice

Push CNN performance with:

  • Data augmentation & normalization
  • Learning rate schedulers & mixed precision training
  • Building a mini-ResNet from scratch
  • Evaluating with confusion matrix & per-class metrics

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Module 5 — Recurrent Neural Networks (RNN)

Model sequential and time-dependent data:

  • Vanilla RNN, LSTM, and GRU explained
  • Sentiment analysis (IMDB dataset)
  • Handling variable-length sequences & padding
  • Teacher forcing & gradient clipping

📖 Read Full Article → (Embed Article 5 link)

Module 6 — RNN for Advanced Use Cases

Master sequence-based deep learning:

  • Character-level language model
  • Time series forecasting with LSTM
  • Comparing RNN vs Transformers

📖 Read Full Article → (Embed Article 6 link)

Module 7 — Capstone Projects

Bring it all together with real-world projects:

  1. CIFAR-10 image classification with ResNet
  2. IMDB sentiment analysis with GRU & embeddings
  3. Tabular churn prediction with ANN embeddings

📖 Read Full Article → (Embed Capstone link)

Series Highlights

  • 💻 100% hands-on PyTorch code
  • 📊 Real datasets (Iris, MNIST, CIFAR-10, IMDB)
  • 🚀 Optimization tricks for faster & better training
  • 🛠️ Deployment-ready model export
  • 📈 Side-by-side comparisons for architecture choice

How to Use This Page

  • Click on any Read Full Article link to access the detailed module.
  • Each article contains:
    • Theory explained in simple terms
    • Step-by-step code walkthrough
    • Exercises to test your learning
    • Best practices & pitfalls to avoid