LeetCode for Machine Learning: Where to Practice ML Coding

Is there a LeetCode for ML? Yes. Several platforms let you practice machine learning by writing code that is checked automatically: implementing algorithms, loss functions, optimizers, neural network layers and, on some platforms, GPU kernels. They differ mainly in what they train: ML fundamentals, guided learning, GPU programming or research-paper implementations.

None of them is an official LeetCode product. “LeetCode for ML” is just the shorthand people use for this kind of practice.

What is LeetCode for machine learning?

People searching for “LeetCode for ML”, “ML LeetCode” or “LeetCode but for ML” usually want three things: problems they can solve in the browser, automatic feedback on their implementation, and a way to get better through repetition.

In practice, machine learning coding practice means implementing concepts from scratch: gradient descent, regression, loss functions, attention and optimization algorithms. You usually work in NumPy or PyTorch, and you practice the math behind the code at the same time. Some platforms go further, into GPU kernels or rebuilding architectures from research papers.

Which platforms are closest to LeetCode for ML?

These four platforms take different approaches. The facts below were checked against each platform's official site on October 16, 2026.

There is no single “LeetCode for ML” that suits everyone. The right choice depends on whether you want broad practice in one place, guided learning, GPU work alone or research implementations.

What makes ML coding practice different from LeetCode?

ML coding practice is not just LeetCode with ML topics. Algorithm problems usually check a solution against exact expected outputs, then look at efficiency. ML problems keep those checks and add a few that ordinary data structures and algorithms practice rarely needs.

Numerical stability

Softmax is the classic example. The direct formula is:

import numpy as np

def softmax(x):
    exp_x = np.exp(x)
    return exp_x / exp_x.sum()

This works for ordinary values but breaks when a logit is large. With an input containing 1000, np.exp(1000) overflows to infinity, and infinity divided by infinity is NaN. A numerically stable version subtracts the largest input first:

import numpy as np

def softmax(x):
    x = x - np.max(x)
    exp_x = np.exp(x)
    return exp_x / exp_x.sum()

Subtracting the same constant from every logit does not change the result mathematically, but it keeps every exponent at or below zero, so nothing overflows.

Answers checked within a tolerance

Floating-point results rarely match to the last digit, because summing the same numbers in a different order can change the final bits. ML problems therefore compare outputs within a tolerance instead of requiring exact equality, and a good test suite includes inputs designed to expose instability, such as very large or very small values.

Shapes and broadcasting

Many ML bugs do not crash. Adding a vector of shape (n,) to a matrix of shape (n, 1) silently broadcasts to (n, n), and the code runs with the wrong answer. Practice problems that test several input shapes catch this kind of mistake early.

Which ML practice platform is best for you?

TensorTonic

Best for: learners who want structured and guided ML fundamentals, GPU kernels, research papers and interview prep in one place.

TensorTonic is an ML coding practice platform where you implement algorithms, CUDA and Triton kernels and research papers from scratch. The free plan includes 500+ problems with unlimited submissions, theory content for every problem, interactive visualizations, 35 CUDA kernel problems, and 16 free research-paper tracks. Toni, an AI assistant built into the editor, helps while you code.

What sets it apart is the combination: graded ML problems, CUDA and Triton kernels benchmarked on six real GPUs, and research papers broken into step-by-step problems, all in one catalog with one progress history. Whether you are completely new to ML or preparing for interviews, and whether you want ML coding, GPU programming or AI research-paper implementations, TensorTonic covers it in one place.

Deep-ML

Best for: learners who want an open-source set of ML problems.

Deep-ML has more than 1,000 free problems, all open-sourced on GitHub. Each problem includes an explanation, starter code, a learning section and test cases, and the site has an AI study assistant. A Premium plan at $16 a month adds more submissions and AI hints.

Its own FAQ is candid about the comparison: asked whether it is the LeetCode for machine learning, it answers “Not quite,” because it is designed to teach ML skills rather than algorithmic problem-solving.

LeetGPU

Best for: learners whose main goal is GPU programming.

LeetGPU is built around GPU programming challenges and a global leaderboard. It has 50+ problems on matrix operations, memory optimization and kernel fusion, and supports CUDA, Triton, PyTorch, Mojo, CuTe DSL and JAX, all running on real GPUs. It is free to use, and a $6.99 a month Pro plan adds H100, H200 and B200 access and higher limits.

The trade-off is scope. LeetGPU teaches GPU programming, not machine learning, so it suits engineers who already know the ML side. If you want to learn the algorithm and then write the kernel for it, a platform that covers both is a better fit.

NeetCode ML

Best for: people who want brief, quick practice.

NeetCode ML is a free set of 36 problems in seven groups, with ten problems that assemble a working GPT.

The trade-off is size and range. Thirty-six problems make a good guided introduction but not a long-term practice library, and GPU programming and research papers are outside its scope.

Where does TensorTonic fit?

TensorTonic covers areas that are usually split across separate resources, so you can go from the math to a working implementation to a GPU kernel without switching platforms.

Interactive visualizations that make concepts click

Most practice platforms show you a problem statement and a test result. TensorTonic also shows you what the math is doing. Lessons and problems come with interactive visualizations you can change and watch respond: drag a learning rate and see gradient descent converge or diverge, adjust attention weights and see how the output changes, or watch a matrix transform space in eigenvectors and eigenvalues. Seeing a concept move before you code it makes the implementation far easier to get right.

The same approach runs through the ML Math lessons and the LLM Internals series, which explains topics such as the KV cache and FlashAttention with interactive diagrams.

CUDA and Triton kernels, benchmarked on real GPUs

The CUDA Kernels and Triton Kernels study plans take you from vector addition to reductions, convolutions, tiled matrix multiplication and attention kernels. Every submission runs against correctness tests on real hardware, and you can compare timings across six GPUs, from a Tesla T4 and RTX 4090 to the H100 and H200. Most platforms either teach ML or teach GPU programming; this is where the two meet.

Research papers, one component at a time

Research tracks turn 23 influential papers into step-by-step problems. The Attention Is All You Need track moves from tokenization and positional encoding to scaled dot-product attention, multi-head attention and the encoder block. Newer tracks cover modern LLMs such as GPT-2, LLaMA and DeepSeek-V3.

Guided projects: build complete AI systems

Single problems teach one component at a time. Projects put the components together into a working system, step by step:

• RAG from Scratch: build retrieval-augmented generation in plain Python, from chunking and retrieval to a complete assistant.
• Build a ReAct Agent: implement tool parsing, dispatch, state, control loops, permissions and agent evaluation.
• Build an MCP Server: implement JSON-RPC requests, tool schemas, argument checks, dispatch and resources.
• Fine-tune an LLM: learn the fine-tuning workflow, then fine-tune Qwen3-4B with QLoRA in a notebook.
• Build an inference server: learn how model servers work, then build one for Qwen3-4B in a notebook.

This is the step most practice platforms skip: going from “I can implement attention” to “I can build and ship a system that uses it”.

Toni, the in-editor AI assistant

Toni lives inside the code editor and sees the problem you are working on, so you can ask for a hint or an explanation without leaving the page. Free accounts get 30 Toni messages a day, and Pro or Plus accounts get 300.

Theory next to every problem

Each problem comes with the theory behind it and interactive visualizations, and 67 ML Math lessons cover the linear algebra, probability, calculus and optimization underneath. Accepted solutions can also sync automatically to a private GitHub repository.

Structured prep when you want it

Paid plans add structured study plans, 500+ quiz questions and mock interview assessments for roles such as Data Scientist, ML Engineer and Research Engineer. Plus adds ML system design problems and the modern LLM paper tracks. You can browse the problem library or compare plans and pricing.

Practice this

Start with a TensorTonic study plan built around the kind of implementation you want to practice:

• Problem library: 500+ free ML coding problems in NumPy and PyTorch, from activation functions to attention.
• CUDA Kernels: 35 kernels in CUDA C++, from vector addition to tiled matrix multiplication.
• Triton Kernels: 30 kernels in Triton, from the block model to attention and LLM kernels.
• Inference Engineering: 30 problems on attention variants, KV caching, quantization and serving.
• Research papers: rebuild the Transformer, ResNet, BERT, GANs and more, one component at a time.
• Projects: build RAG systems, agents, MCP servers and fine-tuned models end to end.

Related guide

Wondering whether implementing ML from scratch is worth the effort at all? Read Why bother learning machine learning from scratch?