What should I know about deep learning before starting my first project?

You should prioritize understanding the underlying mathematical mechanics of loss functions and backpropagation before writing a single line of training code. If you cannot explain the failure modes of your model, your integration tests will never catch the silent gradient issues that cause production instability in distributed deep learning environments.

I remember trying to tune a model for a high-frequency trading signal back in the day, thinking I could just swap out libraries to get better performance without knowing what was actually moving under the hood. It was a disaster because I spent three weeks chasing down latency issues that were actually caused by poorly configured tensor shapes rather than the framework itself.

You need to accept that libraries like Keras or PyTorch are just tools, and if you do not understand the data flow, the library will not save you when your training pipeline crashes. Focus on learning how to feed your data correctly before you worry about the architecture of the layers.

PyTorch is generally better if you want a granular understanding of the training loop, whereas Keras offers a streamlined interface that works well if you need to ship a proof of concept quickly. You will find that PyTorch provides much better debugging capabilities when your model behaves unexpectedly, but Keras significantly reduces the boilerplate code required to set up standard classification tasks.

Stop overthinking the framework choice and just pick one to build something tangible. You are going to be wrong about the architecture anyway, so you might as well learn by breaking things in a environment you can actually run. Pick PyTorch and build a basic classifier, because reading theory without hands-on implementation is a waste of time in this field.

The choice between learning theory and diving into code is a false dichotomy because true proficiency is developed through the iterative feedback loop of implementation and failure. If you start by simply downloading a framework, you will likely encounter black-box behaviors that you cannot debug because you lack the conceptual understanding of how data flows through a computational graph. Most beginners hit a wall when they treat deep learning models as magic boxes, only to find that their training loss remains static due to an improper weight initialization or an incorrect activation function.

You should start by building a single-layer perceptron from scratch using only fundamental linear algebra libraries. This exercise will expose you to the reality of tensor manipulations, which are the bedrock of any production-grade neural network. Once you have successfully coded the forward and backward passes manually, move to a high-level framework to see how they abstract those complexities away. The goal is to reach a point where you can identify whether a performance bottleneck is caused by your infrastructure configuration or by a fundamental flaw in your neural network architecture. By the time you reach the stage of deploying models into a cloud-native platform, you will need that granular knowledge to perform effective troubleshooting, as standard unit tests are insufficient for verifying the behavior of weights and biases in complex, sharded deep learning clusters.

Selecting your tech stack requires evaluating the trade-offs between rapid prototyping and long-term scalability. Keras acts as a high-level abstraction that is excellent for building functional models quickly, whereas PyTorch offers granular control that is usually preferred when you need to optimize memory usage or implement custom training loops for production workloads.