Learn Machine LearningMachine Learning
through code
Implement 1000+ algorithms from scratch, from foundational ML to CUDA kernels, with the math, visualizations, and research papers behind them.
Loss surface
step 0|loss 5.372
- is correct
- every step
- converges
All tests passed ✓
The complete ML engineering platform
Code ML from scratch
Write GPU Kernels
Implement AI/ML research papers
Build Agentic Systems
Ace the interview
loss L(w)|rolling down the gradient
import numpy as np
def linear_regression(X, y, lr=0.01):
w, b = 0.0, 0.0
for _ in range(100):
err = w * X + b - y
w -= lr * (err * X).mean()
b -= lr * err.mean()
return w, bThe complete ML engineering platform
Code ML from scratch
Write the algorithm yourself, run it against real test cases, and see exactly where it breaks. 1000+ problems from gradient descent to GANs.
loss L(w)|rolling down the gradient
import numpy as np
def linear_regression(X, y, lr=0.01):
w, b = 0.0, 0.0
for _ in range(100):
err = w * X + b - y
w -= lr * (err * X).mean()
b -= lr * err.mean()
return w, bWrite GPU Kernels
CUDA and Triton in the browser, compiled and executed on real silicon. From vector add to fused attention — no driver setup, no cloud bills.
C[i][j] = row(A, i)·col(B, j)
__global__ void matmul(
float *A, float *B, float *C) {
__shared__ float tA[T][T];
int r = blockIdx.y * T + ty;
for (int k = 0; k < N; k += T)
acc += tA[ty][k] * tB[k][tx];Implement AI/ML research papers
Transformer, ResNet, BERT, Diffusion — 13 landmark papers reconstructed layer by layer, until the citation becomes comprehension.
implemented paper by paper
Landmark papers, implemented from scratch
Build Agentic Systems
RAG pipelines, tool use, reasoning loops — built in live cloud sandboxes with real LLMs and vector databases. Edit, run, watch the trace change.
reason, act, observe|until the task is done
# ReAct agent loop
while not done:
thought = llm(context)
action = parse(thought)
obs = tools.run(action)
context += obs
done = is_final(obs)Ace the interview
Role-based sheets curated from real interviews at Meta, Google, Airbnb and Uber. Plus ML system design with a live canvas and 500+ quizzes.
build the profile ·train on the labs that hire
What learners are saying
0+
learners worldwide
0Million+
code submissions
0+
problems to solve
Plans
Free
$0forever
Start building your ML foundations
- Unlimited code submissions
- 300+ coding problems
- 35 CUDA / GPU kernel problems
- Interactive visualizations
- Theory content per problem
- 13 research paper implementations
- ML Math blogs
- Community Discord access
- Solutions & explanations
- Structured study plans
- Real-time interview mock assessments
- ML System Design problems
Pro
$12/month
or $100/yr · save 31%
- Everything in Free
- Solutions & explanations to all problems
- Structured roadmap study plansPandas, NumPy, PyTorch, SQL FoundationsLinear Algebra, Probability, Calculus, OptimizationML, DL, NLP, RL, CV specializations
- 500+ quiz questions across 12 modules
- Real-time interview mock assessmentsData Scientist, ML Engineer, AI EngineerResearch Engineer, GPU and Performance
- Priority support
- ML System Design problems
- Modern LLM architectures like GPT2, DeepSeek
Plus
$20/month
or $168/yr · save 30%
- Everything in Pro
- Implement modern LLM architectures like GPT-2, DeepSeek etc in PyTorch
- ML System Design problems
- Interactive design canvas
- FAANG-styled system design scenarios
Questions, answered
Is TensorTonic free to use?
TensorTonic has more than 300+ coding problems, CUDA and GPU kernel exercises, 13 research paper implementations, and the full ML Math library completely free. Premium plans add worked solutions, structured study plan roadmaps, ML system design, and interview prep.
Stop watching.
Start building.
Don't just watch ML theory. Implement it yourself, one algorithm at a time, and understand it in depth.