All roadmapsRoadmap · 2026

Deep Learning Roadmap 2026

The complete Deep Learning roadmap in 7 stages — Foundational Skills, Machine Learning Core, Deep Learning Fundamentals, Advanced Deep Learning, Specialized DL Areas, Deployment and MLOps and Portfolio and Job Prep. Open any stage in the mind map to see the topics and tools inside it, or download the whole roadmap as an image.

Mind map

Deep Learning Roadmap

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Deep Learning roadmap, stage by stage

01Foundational Skills
- Programming Essentials
  - Python programming language
  - NumPy for numerical ops
  - Pandas for data analysis
  - Git and GitHub basics
- Mathematics for DL
  - Linear algebra fundamentals
  - Calculus for optimization
  - Probability and statistics
- Data Handling Basics
  - Data cleaning techniques
  - Data visualization Matplotlib
  - SQL for data querying
02Machine Learning Core
- Supervised Learning
  - Regression algorithms
  - Classification algorithms
  - Scikit-learn library
- Unsupervised Learning
  - Clustering algorithms
  - Dimensionality reduction
- Model Evaluation
  - Metrics for regression
  - Metrics for classification
  - Cross-validation techniques
- Feature Engineering
  - Categorical encoding
  - Numerical scaling
03Deep Learning Fundamentals

Learn the Python, data and AI parts of this roadmap with free video courses, notes and a certificate.

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Neural Network Basics

Perceptrons activation functions
Feedforward networks
Backpropagation algorithm

Deep Learning Frameworks

TensorFlow Keras API
PyTorch library

Training Neural Networks

Loss functions optimizers
Regularization techniques
Hyperparameter tuning

Convolutional Networks

CNN architectures LeNet AlexNet
Image classification tasks
Transfer learning fine-tuning

04Advanced Deep Learning

Recurrent Neural Networks
- RNN LSTM GRU architectures
- Sequence data processing
Transformers Architecture
- Attention mechanism
- BERT GPT models
Generative Models
- GANs VAEs diffusion models
- Image text generation
Reinforcement Learning Intro
- Markov Decision Processes
- Q-learning Policy Gradients

05Specialized DL Areas

Computer Vision Advanced
- Object detection YOLO R-CNN
- Image segmentation U-Net
- Pose estimation
Natural Language Processing
- Text embedding Word2Vec GloVe
- Sentiment analysis NER
- Large Language Models LLMs
Graph Neural Networks
- GNN architectures
- Graph data applications

06Deployment and MLOps

Model Deployment
- REST APIs FastAPI Flask
- Docker for containerization
- ONNX for model interchange
MLOps Practices
- Experiment tracking MLflow
- Model versioning DVC
- CI CD for ML pipelines
Cloud Platforms
- AWS SageMaker
- Google Cloud AI Platform
- Azure Machine Learning

07Portfolio and Job Prep

Project Development
- End-to-end DL projects
- Kaggle competitions
- Open-source contributions
Interview Preparation
- Behavioral questions
- Technical questions coding
- System design questions
Networking and Branding
- LinkedIn profile optimization
- Conference meetups
- Personal website blog