LeNet & AlexNet: The First Wins
Where modern CNNs began.
LeNet & AlexNet: The First Wins is a free Deep Learning Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Where CNNs Began
Long before todays giants, two small networks proved that learned convolutions could read images. Lets meet the pioneers.
LeNet-5 Reads Digits
In 1998 Yann LeCun built LeNet-5 to recognize handwritten digits on checks. It was tiny, but it worked beautifully.
A Simple Recipe
LeNets pattern was clear: alternate convolution and pooling layers, then finish with a few dense layers. That recipe still echoes today.
Trained End to End
The big idea was that LeNet learned its filters from data by backpropagation, instead of an engineer hand-coding edge detectors.
Then Came the Pause
Data and compute were scarce, so progress stalled for years. The world was not yet ready to scale this idea up.
AlexNet Wakes the Field
In 2012 AlexNet crushed the ImageNet contest, halving the error rate and igniting the modern deep learning boom.
Bigger and Deeper
AlexNet was a scaled-up LeNet: more layers, far more filters, and millions of parameters hungry for data.
ReLU Speeds Training
AlexNet swapped slow saturating activations for the fast, simple ReLU, letting a deep net train in a reasonable time.
Trained on Two GPUs
It ran on two GPUs in parallel. That hardware choice turned an old idea into a practical, winning system.
Dropout Fights Overfit
To stop memorizing, AlexNet added dropout in its dense layers, randomly silencing neurons during training.
import torch.nn as nn
classifier = nn.Sequential(nn.Dropout(0.5), nn.Linear(4096, 1000))A Conv Layer in PyTorch
Both nets are stacks of one core block. Here is a single Conv2d layer, the brick everything is built from.
import torch.nn as nn
layer = nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4)Quick Check
Think about what made AlexNet so influential.
Recap: The First Wins
LeNet showed CNNs could learn, and AlexNet proved they could win at scale. Together they launched the era you are studying. Nice start!
Frequently asked questions
Is the “LeNet & AlexNet: The First Wins” lesson free?
Yes — the full text of “LeNet & AlexNet: The First Wins” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “LeNet & AlexNet: The First Wins”?
Where modern CNNs began. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “LeNet & AlexNet: The First Wins” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- LeNet & AlexNet: The First Wins
- VGG: Stacks of Small Filters
- ResNet: Skip Connections Go Deep
- Load torchvision Models