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A
Chapter 3 · Lesson 3IN PROGRESS
Backpropagation & Gradient Flow

Backpropagation is the algorithm that makes neural networks learn. It computes how much each weight in the network contributed to the final error, then nudges every weight in the direction that reduces it.

1

The forward pass

Data flows forward through every layer. Each neuron computes a weighted sum of its inputs and passes the result through an activation function. The final layer produces a prediction.

x
x
Input
h
h
h
Hidden 1
h
h
h
Hidden 2
ŷ
ŷ
Output
2

Computing the loss

After the forward pass, we compare the prediction ŷ to the true label y using a loss function. Mean squared error is common for regression:

L = ½ · (ŷ − y)2
3

The backward pass

Backprop uses the chain rule to compute the gradient of the loss with respect to every weight — layer by layer, working backwards from the output.

Key insight: The gradient tells each weight: "move this much, in this direction, to reduce the loss." That's the entire learning signal.
The problem

AI can answer your questions. But can it teach you?

ChatGPT, Claude, Gemini and other AI models are great at explaining things. But learning is more than asking questions — you need a clear path, the right resources, practice, feedback, and a way to know how far you've come.

A new chat every time, with no memory of what you've already learned
Answers with no structure — no path, no order, no next step
No way to test whether it actually sank in
No sense of progress, so you never know how far you've come
The Lenni way

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A chat with AI
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SKILL LEVEL DETECTION
GitHubREADING
12 public repos
Python · JS · Rust
ML projects detected
LinkedIn
3 yrs experience
Software Engineer
2 ML-related roles
Resume
BSc Computer Science
PyTorch · TensorFlow
Deployed 2 models
Skill test
Scored 74 / 100
Strong: data structures
Weak: optimisers
LENNI CONCLUSION
Intermediate level detected. Your path starts at Chapter 2, skipping basics you already know.
YOUR LEARNING PLAN · AI-generated
Foundations
4 lessons · 2 hrs
Core Concepts
5 lessons · 3 hrs
3
Applied PracticeIN PROGRESS
6 lessons · 3.5 hrs
4
Advanced Topics
4 lessons · 2.5 hrs
5
Final Project
2 lessons · 1 hrs
21 lessons · ~12 hours total40% complete
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Chapter 4 · Lesson 1
Attention Mechanisms
Chat to ask anything
L
Attention lets the model weigh every token against every other — that's why Transformers capture long-range dependencies so well.
U
Why can't RNNs do the same thing?
L
RNNs pass context through a hidden state, so distant information gets diluted. Attention looks at everything at once, no forgetting.
U
That makes sense. What's the time complexity trade-off?
L
Ask anything about this lesson…
EXERCISE · Chapter 4
AI-generated quiz
Q 2 of 5
What is the time complexity of the self-attention mechanism with respect to sequence length?
Select an answer to see Lenni's feedback.
Exercises & evaluation

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Progress tracking

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Lenni tracks every lesson, quiz score, and learning streak. Your dashboard shows which skills are growing, where you're strongest, and what needs more attention — so you always know what to do next.

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PROGRESS TRACKER
14
Lessons
11
Quizzes
82%
Avg score
7d
Streak
DAILY ACTIVITY
M
T
W
T
F
S
S
SKILL GROWTH
ML Theory
82%
Python / NumPy
70%
Model Training
55%
Evaluation
38%
How it works

From goal to mastery in four steps.

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Claude & GPT-4
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FAQ

Questions people actually ask.

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