5 Common Mistakes People Make When Learning Deep Learning (and How to Avoid Them)

Deep learning sounds exciting, but learning it can feel like hitting a wall. Not because it’s impossible, but because most people approach it the wrong way.

If you’ve started a deep learning course online (or three) and still feel stuck, you’re not alone.

Here are five of the most common mistakes beginners make while learning deep learning and how you can avoid them without wasting months in confusion.

Why shouldn't you skip machine learning basics before learning deep learning?

This is probably the biggest one.

Many learners start with CNNs or transformers without a solid grip on the core math or basic machine learning concepts. But deep learning builds on those foundations. If you don’t understand linear regression or gradient descent, you’re going to get lost quickly.

Avoid it by:

• Starting with machine learning basics (regression, decision trees, overfitting)
  
  
• Learning Python + NumPy well before touching TensorFlow or PyTorch
  
  
• Studying how neural networks learn, not just what layers to use
  
  

Is coding the only thing that matters when learning deep learning?

Deep learning isn’t just code. It’s math, theory, data, intuition, and problem framing. You can’t just copy-paste from GitHub and expect to “learn.”

Many beginners skip the why and only focus on the how then hit a wall when something doesn’t work.

Avoid it by:

• Learning the concepts behind loss functions, activation functions, and optimizers
  
  
• Running fewer tutorials, but understanding each line
  
  
• Reading papers or watching visual explanations of concepts
  
  

Why do people get stuck in the tutorial trap when learning deep learning?

Tutorials are helpful until they’re not.

If you’ve followed 10 YouTube videos and still can’t build your own model from scratch, you’re in tutorial trap mode. You’re consuming content, not applying it.

Avoid it by:

• Picking a small project with real-world data (like image classification or text generation)
  
  
• Forcing yourself to build without copying code
  
  
• Debugging your own errors, that’s where real learning happens
  
  

Why is understanding your data just as important as building models?

You can’t learn deep learning without getting your hands dirty with data.

A lot of beginners focus so much on model architecture that they forget how critical data preprocessing, augmentation, and splitting is. If your data is trash, your model will be too, no matter how fancy it looks.

Avoid it by:

• Spending time understanding the dataset before you even write code
  
  
• Practicing with small, messy datasets, not just curated ones
  
  
• Learning how to spot data leakage or imbalance early on
  
  

Can choosing the wrong deep learning course slow down your progress?

Too many people pick deep learning courses that are either too theoretical, too fast-paced, or too fragmented. It burns them out before they build any confidence.

Avoid it by:

• Choosing structured, beginner-friendly deep learning courses online
  
  
• Looking for programs that focus on both concepts and projects
  
  
• Ensuring you have access to guidance, not just videos
  
  

If you're looking for direction, here are some of the Best Online Courses for Deep Learning that avoid all of the mistakes listed above.

Final Thought

Deep learning isn’t just for PhDs or engineers at big tech companies. But it’s not plug-and-play either. The key is to learn slowly, apply what you learn immediately, and stop trying to rush through it.

You don’t need 50 hours a week. You just need the right mindset and a course that teaches how to think, not just what to code