- Dr. Anya Sharma argues building ML algorithms in pure Python fosters a working understanding of how models actually learn.
- Implementing algorithms like gradient descent from scratch forces direct engagement with underlying calculus, clarifying model failures.
- Engineers who trace model behavior to its mathematical roots are better positioned to debug failures and adapt algorithms.
Most ML engineers have never written a gradient descent loop by hand. Dr. Anya Sharma, a lead researcher at the Allen Institute for AI, thinks that gap is starting to matter. In a recent blog post titled “Beyond the Black Box: Why Understanding ML Fundamentals Matters More Than Ever,” Sharma argues that building core algorithms in pure Python, before reaching for TensorFlow or PyTorch, produces engineers who can actually reason about why a model fails, not just redeploy it.
Building core algorithms in pure Python, before reaching for TensorFlow or PyTorch, produces engineers who can actually reason about why a model fails, not just redeploy it.
Why Bare-Bones ML Matters Now
Sharma’s argument is not anti-framework. It is pro-understanding. High-level libraries are efficient precisely because they abstract away the details: matrix multiplications, derivative calculations, weight updates. That efficiency is a feature until something goes wrong, at which point the abstraction becomes a wall. Her case is that engineers who have built these operations explicitly, in NumPy or plain Python, have a mental model of what is happening inside the wall. Those who have not are left reading documentation and guessing.
The Essence of Pure Python Implementations
Building even a simple linear regression model from scratch makes the process concrete. You calculate the hypothesis, compute a cost function and update weights and biases through gradient descent, all explicitly, in code you wrote. There is no .fit() call to hide the steps. A basic neural network implementation goes further: forward propagation, loss calculation, then backpropagation by hand to adjust every parameter. Writing it out forces you to confront exactly how data moves through the model and where learning actually occurs. NumPy handles the numerical operations, but the logic is yours.
Unpacking Core Algorithms: A Deeper Dive
Logistic regression is a useful test case. A from-scratch implementation starts with the sigmoid function, which squashes output values to a range between 0 and 1 for binary classification. Then you write the cost function, typically binary cross-entropy, which measures the gap between predicted and actual outputs. The critical part is deriving the gradient descent update rules for weights and bias, which requires working through the partial derivatives of the cost function with respect to each parameter.
That last step is where most library users have never been. It is also where understanding pays off in practice. Engineers who have done it know intuitively why a learning rate that is too high causes divergence, and why feeding unscaled features into a model can destabilise training. The calculus is not academic. It explains the behaviour you observe in production.
Beyond Abstraction: Debugging and Optimisation Advantages
When a framework model misbehaves, tracing the error through layers of abstraction is slow. An engineer who has built a comparable algorithm from scratch can move faster, because they know which components to check: gradient magnitudes, learning rate behaviour, scaling on the input data. The failure modes are familiar.
The same foundation supports more targeted optimisation. Knowing how gradient descent is implemented makes it practical to experiment with custom optimisers, tune learning rate schedules to specific data characteristics, or apply regularisation techniques that high-level APIs do not expose directly. In constrained environments, or on problems where standard methods underperform, that kind of low-level access can produce meaningful gains that off-the-shelf configurations cannot.
Equipping the Next Generation of ML Engineers
As large language models and agentic AI systems grow more complex, the ability to reason about model internals, not just operate them, becomes harder to substitute. Companies building at the frontier need engineers who can identify performance bottlenecks at a granular level and adapt algorithms to problems that do not fit neatly into existing tooling. That is not a skill frameworks teach. It comes from having written the underlying operations yourself. Sharma’s post is one person’s argument, not a field-wide mandate, but the case it makes is straightforward: the engineers who understand what the abstraction is hiding are the ones best placed to work around it when it fails. For more coverage of AI research and breakthroughs, visit our AI Research section.
