Neural Networks for Everyone
Build a clear understanding of neural networks through concise explanations, meaningful real-world examples, and a carefully ordered learning path.
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15 modules, 82 lessons
Follow the modules in order, or open any lesson to review a specific concept. Lessons 71–82: 12 videos available. Every lesson includes reading and practice; published videos have optional captions and transcripts.
Neural Network Foundations
Lessons 1–6
What Is a Neural Network?
Learn how a neural network turns numerical inputs into predictions and improves through feedback.
Inputs, Weights, Bias, and Neurons
Learn how small pieces of information become one decision.
Activation Functions
Meet the gate that decides which signals move forward.
Layers and Deep Learning
Follow simple features as they become complete objects.
How a Neural Network Learns
See how feedback helps a network improve its weights.
Classification, Regression, and Clustering
Compare three common ways computers learn from data.
Module 1 quiz
5 questions · score and feedback · retry anytime
Regression Loss Functions
Lessons 7–11
From Prediction to Loss
Learn how neural networks measure mistakes and use loss to improve.
Calculating Mean Squared Error
Calculate MSE step by step with five house-price predictions.
Why Squaring Changes Learning
See why MSE emphasizes large errors and how outliers can dominate learning.
Mean Absolute Error
Measure average prediction distance without allowing signs to cancel.
Huber Loss
Combine smooth learning for small errors with gentler treatment of outliers.
Module 2 quiz
5 questions · score and feedback · retry anytime
Classification Loss and Training
Lessons 12–16
Binary Cross-Entropy
See how AI learns reliable yes-or-no decisions.
Categorical Cross-Entropy
Learn how a model chooses one class from several options.
Hinge Loss and the Safety Margin
Understand why a correct decision may still need more separation.
The Neural Network Training Loop
Follow prediction, loss, backpropagation, and weight updates.
Epochs, Batches, and Convergence
Organize training examples and recognize when learning stabilizes.
Module 3 quiz
5 questions · score and feedback · retry anytime
Generalization and Regularization
Lessons 17–22
Generalization
Learn why success on unseen data is the real test of a neural network.
Overfitting
See why excellent training scores can hide poor real-world performance.
Dropout
Train a network that does not depend too heavily on a few neurons.
L1 Regularization
Encourage a simpler neural network by shrinking unnecessary weights to zero.
L2 Regularization
Reduce extreme weights while retaining useful signals across many features.
Data Augmentation
Create realistic training variety while preserving correct labels and annotations.
Module 4 quiz
5 questions · score and feedback · retry anytime
Model Fit and Data Scaling
Lessons 23–28
Underfitting
Learn what happens when a neural network is too simple to capture the real pattern.
Bias and Variance
Understand the two forces behind underfitting, overfitting, and model generalization.
Why Normalization Matters
See why features measured on different scales can make neural-network training difficult.
Min-Max Normalization
Transform values into a consistent range while preserving their order.
Z-Score Normalization
Measure how far each value lies above or below the dataset average.
Image Normalization and Smoothing
Prepare pixel values and noisy signals without erasing the information a model needs.
Module 5 quiz
5 questions · score and feedback · retry anytime
Learning Curves and Diagnosis
Lessons 29–32
Reading Learning Curves
Use training and validation curves to understand how a neural network changes across epochs.
Diagnosing High Bias
Recognize when a model is too restricted to learn the important pattern.
Diagnosing High Variance
Recognize when strong training performance fails to transfer to new examples.
Balancing Bias and Variance
Use controlled evidence to find a model that learns well and generalizes reliably.
Module 6 quiz
5 questions · score and feedback · retry anytime
Backpropagation and Gradients
Lessons 33–36
What Backpropagation Actually Does
Follow prediction error backward to see how each trainable parameter receives a useful correction.
Gradients and Derivatives
Interpret derivatives as local rates of change and gradients as directions in a multi-parameter loss landscape.
Partial Derivatives and the Chain Rule
Isolate each parameter's influence and connect effects across a sequence of neural-network operations.
Backpropagation Step by Step
Trace one complete forward and backward pass through a small six-weight neural network.
Module 7 quiz
5 questions · score and feedback · retry anytime
Stable Deep-Network Training
Lessons 37–40
Why Weight Initialization Matters
Break neuron symmetry and begin training with diverse signals at a useful numerical scale.
Xavier and He Initialization
Match the initial weight scale to layer connectivity and activation behavior for healthier signal flow.
Batch Normalization
Stabilize internal layer values while preserving the scale and shift required by the task.
Vanishing, Exploding, and Dying Gradients
Recognize unstable gradient flow and select targeted methods that keep deep networks trainable.
Module 8 quiz
5 questions · score and feedback · retry anytime
Convolutional Neural Networks
Lessons 41–56
Introduction to Convolutional Neural Networks
Learn how convolutional neural networks detect visual patterns and build image predictions.
Spatial Structure in Images
Understand why the positions and relationships of visual features matter.
How CNNs Build a Feature Hierarchy
See how CNN layers combine edges and textures into meaningful objects.
Local Connections and Shared Weights
Learn how CNNs reduce parameters by examining local regions and reusing filters.
Filters and Convolution
See how learnable kernels scan images and respond to useful patterns.
Calculating a Convolution
Calculate one convolution output through multiplication and addition.
Feature Maps and Multiple Filters
Understand how filters create spatial maps of learned visual responses.
Stride and Feature Map Size
Learn how filter movement controls spatial detail, computation, and output dimensions.
Padding: Same and Valid Convolution
Understand how padding handles image boundaries, compare same and valid convolution, and calculate output sizes with a clear seven-by-seven example.
Filter Size and Visual Context
Compare CNN filter sizes using a ceramic-inspection example, calculate output dimensions, and see how stacked small filters gather wider context.
Max Pooling Step by Step
Follow a 4 × 4 feature map through 2 × 2 max pooling, calculate its output, and understand what pooling preserves and discards.
Average Pooling and Max Pooling
Compare maximum and average pooling with worked numerical examples and a solar-inspection analogy.
Flattening Feature Maps
Turn several feature maps into an ordered vector while preserving their values and keeping separate images separate.
Fully Connected Layers and Class Predictions
Combine image features into class scores, count weights and biases, and interpret softmax predictions carefully.
LeNet and a Complete Convolutional Network
Trace a simplified modern LeNet-style classifier from a 32 × 32 grayscale input to ten digit scores.
AlexNet and Large Scale Image Classification
Understand AlexNet’s role in large-scale classification and distinguish class labels from object locations and pixel masks.
Module 9 quiz
5 questions · score and feedback · retry anytime
Semantic Segmentation
Lessons 57–62
Semantic Segmentation and Pixel Labels
Use a beach survey to understand pixel labels, aligned target masks, and the difference between categories and individual objects.
Fully Convolutional Networks for Segmentation
Follow an aerial farm image through shared spatial processing, class scores, and an encoder–decoder segmentation network.
Upsampling and Max Unpooling
Compare nearest-neighbor copying, bilinear interpolation, and max unpooling with small numerical examples.
Transposed Convolution Step by Step
Calculate a transposed convolution by spreading weighted contributions, offsetting them by the stride, and adding overlaps.
Understanding the U-Net Architecture
Use cell microscopy to connect U-Net’s encoder, decoder, skip connections, and channel concatenation.
SegNet and U-Net Decoder Designs
Compare U-Net feature transfers with SegNet’s use of pooling indices to guide decoder upsampling.
Module 10 quiz
5 questions · score and feedback · retry anytime
Object Detection
Lessons 63–70
What Is Object Detection?
Learn how object detection combines recognition with location, then compare boxes with image labels and pixel masks.
Bounding Boxes and Coordinates
Turn a rectangle into corner coordinates, center coordinates, dimensions, and normalized values using one worked example.
Intersection over Union
Calculate intersection over union, interpret overlap thresholds, and understand why correct class labels and matching rules also matter.
Confidence Scores and Thresholds
Use confidence thresholds to filter predictions, balance false alarms and missed objects, and distinguish confidence from IoU.
Non-Maximum Suppression
Follow non-maximum suppression through candidate scores and overlap comparisons, including class-aware and crowded-scene limitations.
Precision and Recall for Detection
Count true positives, false positives, and false negatives; calculate precision and recall; interpret their practical trade-offs.
Average Precision and Mean Average Precision
Connect precision–recall curves to AP and mAP, then compare AP50, AP75, and COCO’s averaged overlap thresholds.
One-Stage and Two-Stage Detectors
Compare one-stage and two-stage detectors, understand shared features and region proposals, and measure quality alongside processing time.
Module 11 quiz
5 questions · score and feedback · retry anytime
Transfer Learning and Representations
Lessons 71–73 · 3/3 videos available
Transfer Learning: Start with Useful Features
You have a small collection of labeled images, but training a large network from scratch would require far more data and computation.
Fine-Tuning Without Forgetting Everything
The new classification head is working, but some target images still look different from the data used to pretrain the backbone.
Embeddings: Represent Similarity with Vectors
You want to find images that resemble one example without defining every possible category.
Module 12 quiz
5 questions · score and feedback · retry anytime
Sequences and Attention
Lessons 74–77 · 4/4 videos available
Recurrent Networks: Carry Information Through a Sequence
The words dog bites person and person bites dog contain the same words but mean different things.
LSTM and GRU: Control What the Network Remembers
A useful memory needs more than the ability to keep everything.
Attention: Select the Information That Matters Now
When answering a question about a paragraph, you do not give every word equal attention.
Transformers: Build with Attention and Position
Attention gives us a way to connect positions directly.
Module 13 quiz
5 questions · score and feedback · retry anytime
Autoencoders and Generative Models
Lessons 78–79 · 2/2 videos available
Autoencoders and Useful Compression
Could a network summarize an image and then rebuild it? An autoencoder trains an encoder to produce a representation and a decoder to reconstruct the input.
Generative Models: Plausible Does Not Mean Observed
A generated image can look convincing without depicting an event that ever happened.
Module 14 quiz
5 questions · score and feedback · retry anytime
Deployment, Monitoring, and Final Project
Lessons 80–82 · 3/3 videos available
Deployment: Package the Whole Neural-Network System
Your network works in a notebook, but deployment introduces new ways to fail.
Monitoring, Corrections, and Retraining
A deployed model does not keep learning merely because people use it.
Final Project: From Labeled Data to a Reliable Model
We have followed neural networks from individual weighted connections to detection, attention, and deployment.
Module 15 quiz
5 questions · score and feedback · retry anytime