If you’re serious about Machine Learning – beyond just using libraries and pre-trained models – you need to read foundational papers. Not because they’re old or famous, but because they change the way you think. These papers sharpen your intuition about how and why things work. I picked these five because they’ve directly shaped my work -from building pipelines to deploying real models. Let’s jump in.
1. Gradient-Based Learning Applied to Document Recognition – Yann LeCun et al., 1998
Context:
Back in the 90s, neural networks were falling out of fashion. They were seen as unreliable, slow, and not much better than handcrafted features. Then LeCun’s team showed how CNNs could learn to read handwritten zip codes and digits better than any feature-engineering approach. This paper is the blueprint for how to handle spatial patterns in data.
Main idea:
CNNs use local connections (convolutions) and shared weights to detect patterns like edges, shapes, and textures – which get hierarchically combined into higher-level features.
Where it lives today:
Every modern image classification, object detection, or segmentation model stands on this idea. Even vision transformers reinterpret this same “hierarchical pattern extraction” in a different way.
Action step:
Try building a simple CNN from scratch (no frameworks!) for MNIST or CIFAR-10. You’ll truly “feel” what convolutions, pooling, and weight sharing do.
Link of Paper: Click to Read
2. ImageNet Classification with Deep Convolutional Neural Networks” – Krizhevsky, Sutskever & Hinton, 2012
Context:
Dubbed “AlexNet”, this paper changed the game overnight. The ImageNet competition was a benchmark for recognizing thousands of object categories. AlexNet’s use of ReLU activations, GPU parallelization, and dropout to reduce overfitting smashed previous error rates by a huge margin.
Main idea:
Bigger datasets + bigger models + better hardware = breakthroughs. The architecture was relatively simple, but the engineering made it trainable at scale.
Where it lives today:
You can trace today’s deep ResNets, EfficientNets, and even some aspects of transformers back to this “scale up” mindset.
Action step:
Read the paper’s section on training tricks – weight initialization, data augmentation, and local response normalization – they’re still gold.
Link of Paper: Click to Read
3. Attention Is All You Need” – Vaswani et al., 2017
Context:
Before this, RNNs and LSTMs ruled NLP. They were slow, hard to parallelize, and struggled with long-range dependencies. The Transformer said: “What if we get rid of recurrence entirely?” The answer was self-attention: a way for each word to look at every other word in the sequence.
Main idea:
Self-attention lets models capture relationships between words, no matter their distance. Plus, it parallelizes beautifully on GPUs.
Where it lives today:
Every major LLM – BERT, GPT, T5, RoBERTa – is built on this. Even non-text domains (images, protein folding, audio) have adapted transformers.
Action step:
Try coding a toy self-attention mechanism for short text. Visualize the attention weights – it’s mind-opening.
Link of Paper: Click to Read
4. Playing Atari with Deep Reinforcement Learning” – Mnih et al., 2013
Context:
This is the Deep Q-Network (DQN) paper. Until this, reinforcement learning (RL) agents relied on manual feature extraction. DQN showed you could train an agent end-to-end – raw pixels in, actions out – to play Atari games at human-level performance.
Main idea:
Combining deep learning with Q-learning enables learning directly from high-dimensional input spaces. Replay buffers and target networks help stabilize training.
Where it lives today:
Modern RL for robotics, AlphaGo, autonomous driving – they all extend these ideas. The concept of “experience replay” alone is now standard.
Action step:
Fire up OpenAI Gym and implement a simple DQN for Pong or Breakout. It’s a rite of passage.
Link of Paper: Click to Read
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift” – Ioffe & Szegedy, 2015
Context:
Deeper networks often suffered from vanishing or exploding gradients. Training was slow, unstable, and needed careful initialization. BatchNorm addressed this by normalizing layer inputs, making training faster and more reliable.
Main idea:
By normalizing activations during training, you reduce “internal covariate shift” – the problem where the distribution of each layer’s input keeps changing.
Where it lives today:
BatchNorm, LayerNorm, and other normalization tricks are used in almost every deep model. Transformers use LayerNorm instead, but the core idea is the same.
Action step:
Try training a deep net with and without BatchNorm. You’ll appreciate how it lets you use larger learning rates and speeds up convergence.
Link of Paper: Click to Read
Closing Thoughts
When you read these papers, don’t just skim the math. Look at the why: the problem they were solving, the tricks they used, and what you’d do differently today. Next time you see a flashy new model, ask yourself: Is this really new, or just an evolution of one of these ideas?
What’s Next?
I’ll follow this up with:
- 5 underrated ML papers that deserve more attention.
- How to actually read a research paper effectively.
- My personal notes on building toy versions of these models.
Your Turn
Which paper changed how you think?
Share it with me – I’m always hunting for classics I might have missed.