From Zero to Framework Expert in 30 Days

Follow our structured curriculum designed by experts. Each module builds on the previous one, ensuring you understand every concept deeply.

1

Week 1-2: Foundations

Understanding Tensors & Operations

Building Basic Math Functions

Memory Management & Broadcasting

Estimated time: 2-3 hours/day

2

Week 3: Automatic Differentiation

Computational Graphs

Backpropagation Engine

Gradient Computation

Estimated time: 3-4 hours/day

3

Week 4: Neural Networks

Layers & Modules

Optimizers & Training Loops

Model Serialization

Estimated time: 4-5 hours/day

Start Your Learning Journey

Video Course Coming Soon

Learn Through Video Tutorials

Follow along as we build TensorWeaver from the ground up. Each video breaks down complex concepts into digestible, hands-on lessons perfect for visual learners.

Video Course in Development

We're preparing a comprehensive YouTube series that will take you through building TensorWeaver step-by-step. Each episode will focus on a specific component with live coding and detailed explanations.

Planned Episode Topics:

1

Tensor Fundamentals

Basic operations and memory management

2

Computational Graphs

Building the foundation for autograd

3

Automatic Differentiation

Implementing backpropagation engine

4

Neural Network Layers

Building modular components

Content creation in progress • No videos available yet

Live Coding Sessions

Watch every line of code being written with detailed explanations of design decisions and implementation details.

Framework Architecture

Understand the big picture: how all components work together to create a complete deep learning framework.

Interactive Q&A

Join the discussion in comments, ask questions, and learn from the community of fellow framework builders.

Get Early Access

Be the first to know when the YouTube series launches

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Updates only

Try TensorWeaver Live

Experience the power of building your own deep learning framework. Edit the code below and see results instantly.

basic_operations.py

Code

import tensorweaver as tw
import numpy as np

# Create tensors - the foundation of everything
x = tw.Tensor([1.0, 2.0, 3.0])
y = tw.Tensor([4.0, 5.0, 6.0])

# Basic operations - see how they work internally
result = x + y * 2
print(f"Result: {result.data}")

# Matrix operations
matrix_a = tw.Tensor([[1, 2], [3, 4]])
matrix_b = tw.Tensor([[5, 6], [7, 8]])
product = matrix_a @ matrix_b
print(f"Matrix product: {product.data}")

Output

$ python basic_operations.py

Result: [ 9. 12. 15.]
Matrix product: [[19 22]
[43 50]]

💡 What's happening: Unlike PyTorch's C++ backend, you can step through every line of TensorWeaver's pure Python code to see exactly how tensor operations work.

Code

import tensorweaver as tw

# Enable gradient tracking
x = tw.Tensor([2.0], requires_grad=True)
y = tw.Tensor([3.0], requires_grad=True)

# Build a computation graph
# f(x, y) = x² + 2xy + y²
result = x**2 + 2*x*y + y**2

# Automatic differentiation
result.backward()

print(f"f({x.data[0]}, {y.data[0]}) = {result.data[0]}")
print(f"∂f/∂x = {x.grad.data[0]}")
print(f"∂f/∂y = {y.grad.data[0]}")

Output

$ python autograd_demo.py

f(2.0, 3.0) = 25.0
∂f/∂x = 10.0
∂f/∂y = 10.0

🔥 The Magic: Watch how the computational graph builds up during forward pass, then see gradients flow backward through each operation. Every step is visible!

Code

import tensorweaver as tw

# Define a simple neural network
class SimpleNet(tw.Module):
    def __init__(self):
        self.layer1 = tw.Linear(2, 4)
        self.layer2 = tw.Linear(4, 1)
    
    def forward(self, x):
        x = tw.relu(self.layer1(x))
        return self.layer2(x)

# Create model and data
model = SimpleNet()
x = tw.Tensor([[1.0, 2.0]])  # Input
target = tw.Tensor([[3.0]])      # Expected output

# Forward pass
output = model(x)
loss = tw.mse_loss(output, target)

print(f"Prediction: {output.data[0][0]:.3f}")
print(f"Loss: {loss.data:.3f}")

Output

$ python neural_network.py

Prediction: 1.247
Loss: 3.071

🧠 Deep Dive: See how layers are implemented, how parameters are stored, and how gradients flow through the entire network. No black boxes!

💡 Pro tip: Use your browser's developer tools to step through the code

Try full examples on GitHub →

Why Choose TensorWeaver for Learning?

Compare the learning experience across different approaches to understanding deep learning frameworks.

Learning Method

TensorWeaver

PyTorch Source

Tutorials Only

Code Readability

Pure Python

C++/CUDA

No code

Complete Implementation

All features

Complex

Fragmented

Learning Curve

Gentle

Steep

Superficial

Time to Understanding

30 days

6+ months

Never

Perfect for Deep Learning Students & Professionals

See what makes TensorWeaver ideal for developers who want to understand framework internals

PhD Students

Research & Understanding

Perfect for researchers who need to understand autograd internals for novel architectures. The clean Python implementation makes concepts that are abstract in papers completely concrete and debuggable.

ML Engineers

Production Debugging

Essential for professionals who debug complex training issues in production. Understanding framework internals helps optimize models, identify bottlenecks, and implement custom operations with confidence.

CS Professors

Teaching Tool

Ideal for educators teaching deep learning courses. Students can see every operation in readable Python, bridging the gap between theory and implementation. Makes abstract concepts tangible and debuggable.

Self-Taught Developers

Career Transition

Perfect for career changers from non-CS backgrounds. The step-by-step progression from basic tensors to neural networks provides a solid foundation for understanding modern ML frameworks and building custom solutions.

Research Scientists

Team Training

Valuable for teams onboarding new hires in ML research labs. The transparent implementation helps researchers understand production PyTorch code faster and implement novel architectures with deeper understanding.

Senior Developers

Architecture Design

Essential for architects designing ML systems at scale. Understanding framework internals is crucial for making informed decisions about performance, memory usage, and distributed training architectures.

Meet Your Instructor

Learn from a Google Developer Expert with 10+ years in ML

Xiaoquan Kong

G

Google Developer Expert

As the creator of TensorWeaver, I'm passionate about making deep learning accessible and understandable. With 10+ years of experience in Machine Learning, I've worked on AI systems and contributed to open-source ML projects.

I'm a Google Developer Expert (GDE) in Machine Learning, an active contributor to the ML community, and currently pursuing a Master's degree in AI for Product Innovation at Duke University. I believe in learning through implementation and transparent code.

TensorWeaver represents my commitment to bridging the gap between theoretical concepts and practical implementation. Every line is designed to be readable, debuggable, and educational—showing you exactly how modern deep learning frameworks work under the hood.

Ready to Master Deep Learning Internals?

Transform your understanding of deep learning frameworks with hands-on implementation. Start your journey today - completely free and open source.

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Master Deep Learning Internals

Learning Frameworks is Hard

Black Box Complexity

Overwhelming Codebase

No Learning Path

TensorWeaver Makes it Easy

100% Transparent Code

Digestible Complexity

Structured Curriculum

From Zero to Framework Expert in 30 Days

Week 1-2: Foundations

Week 3: Automatic Differentiation

Week 4: Neural Networks

Learn Through Video Tutorials

Video Course in Development

Planned Episode Topics:

Live Coding Sessions

Framework Architecture

Interactive Q&A

Get Early Access

Try TensorWeaver Live

Code

Output

Code

Output

Code

Output

Why Choose TensorWeaver for Learning?

Perfect for Deep Learning Students & Professionals

Meet Your Instructor

Xiaoquan Kong

Ready to Master Deep Learning Internals?