Deep Learning Roadmap for Freshers
A comprehensive 10-week learning plan to master Deep Learning, neural networks, and AI models from scratch
This roadmap assumes 3-4 hours of daily study (2 hours learning + 1-2 hours practice)
FocusThis roadmap sequences topics so each day builds on the last—skip ahead only after exercises feel easy.
PracticeBlock time for practice: reading without coding rarely sticks for technical skills.
AudienceBeginners, career switchers, and upskilling professionals can all follow at their own pace.
Week 1-2: Python & Math Fundamentals
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 1: Python Basics for Deep Learning | ||||
| Day 1 |
Python Introduction - Installation & Setup - Jupyter Notebooks - Basic Syntax |
2 | 1 | Python Environments, Variables |
| Day 2 |
Data Structures - Lists, Tuples - Dictionaries, Sets - NumPy Arrays |
2 | 1.5 | NumPy Operations |
| Day 3 |
Data Manipulation - Pandas DataFrames - Data Cleaning - Data Visualization |
2 | 2 | Data Preprocessing |
| Day 4 |
Math Fundamentals I - Linear Algebra Basics - Vectors & Matrices - Matrix Operations |
2.5 | 2 | Matrix Multiplication |
| Day 5 |
Math Fundamentals II - Calculus Basics - Derivatives & Gradients - Partial Derivatives |
2.5 | 1.5 | Gradient Calculation |
| Day 6 |
Practice Day - Data Processing Project - Math Implementation |
1 | 3 | NumPy Implementation |
| Day 7 |
Review Day - Week 1 Concepts - Q&A Session |
1 | 2 | Common Math Operations |
| Week 2: Machine Learning Fundamentals | ||||
| Day 8 |
ML Introduction - What is Machine Learning? - Types of ML - Applications |
2.5 | 1.5 | Supervised vs Unsupervised |
| Day 9 |
Regression Models - Linear Regression - Polynomial Regression - Evaluation Metrics |
2.5 | 1.5 | Loss Functions |
| Day 10 |
Classification Models - Logistic Regression - Decision Trees - Evaluation Metrics |
2.5 | 1.5 | Classification Metrics |
| Day 11 |
Introduction to Neural Networks - Biological vs Artificial Neurons - Perceptron Model - Activation Functions |
2.5 | 1.5 | Activation Functions |
| Day 12 |
Practice Day - Build ML Models - Scikit-learn Implementation |
1 | 3 | Model Training Process |
| Day 13 |
Review Day - Week 2 Concepts - Q&A Session |
1 | 2 | Concept Integration |
Week 3-6: Neural Networks & Deep Learning Fundamentals
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 3-4: Deep Learning Frameworks | ||||
| Day 15 |
TensorFlow/Keras Basics - Installation & Setup - Tensor Operations - Basic Model Building |
2.5 | 2 | Tensor Operations |
| Day 16 |
PyTorch Basics - Installation & Setup - Tensors & Autograd - Basic Model Building |
3 | 2 | Automatic Differentiation |
| Day 17 |
Feedforward Networks - Multilayer Perceptrons - Forward Propagation - Implementation |
3 | 2 | Network Architecture |
| Day 18 |
Training Neural Networks - Backpropagation - Gradient Descent - Optimization Algorithms |
2.5 | 2 | Backpropagation Math |
| Day 19 |
Optimizers & Regularization - SGD, Adam, RMSprop - L1/L2 Regularization - Dropout, Batch Norm |
2.5 | 2 | Preventing Overfitting |
| Day 20 |
Practice Day - Build a Neural Network - MNIST Classification |
1 | 3 | Hyperparameter Tuning |
| Day 21 |
Review Day - Concepts Review - Q&A Session |
1 | 2 | Framework Comparison |
| Week 5-6: Advanced Neural Architectures | ||||
| Day 22 |
Convolutional Neural Networks - CNN Architecture - Convolution Operations - Pooling Layers |
3 | 2 | Feature Extraction |
| Day 23 |
CNN Applications - Image Classification - Transfer Learning - Popular Architectures |
3 | 2 | Transfer Learning |
| Day 24 |
Recurrent Neural Networks - RNN Architecture - LSTM & GRU Cells - Sequence Modeling |
2.5 | 2 | Sequence Processing |
| Day 25 |
RNN Applications - Text Generation - Sentiment Analysis - Time Series Prediction |
2.5 | 2 | Sequence Prediction |
| Day 26 |
Practice Day - CNN Project (Image Classification) - RNN Project (Text Generation) |
1 | 3 | Model Evaluation |
| Day 27-28 |
Review & Projects - DL Concepts - Mini Projects |
1 | 4 | Project Deployment |
Week 7-10: Advanced Deep Learning & Specializations
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 7-8: Advanced Architectures | ||||
| Day 29 |
Autoencoders - Encoder-Decoder Architecture - Applications - Variational Autoencoders |
3 | 2 | Dimensionality Reduction |
| Day 30 |
Generative Adversarial Networks - GAN Architecture - Generator & Discriminator - Training Challenges |
3 | 2 | Adversarial Training |
| Day 31 |
Transformers & Attention - Attention Mechanism - Transformer Architecture - Self-Attention |
3 | 2 | Attention Mechanisms |
| Day 32 |
Transfer Learning - Pre-trained Models - Fine-tuning Techniques - Model Zoo Usage |
3 | 2 | Leveraging Pre-trained Models |
| Day 33 |
Practice Day - Build a GAN Model - Transformer Implementation |
1 | 3 | Model Training Challenges |
| Day 34 |
Review Day - Advanced Concepts - Q&A Session |
1 | 2 | Architecture Comparison |
| Week 9-10: Specializations & Deployment | ||||
| Day 35-37 |
Computer Vision Specialization - Object Detection - Image Segmentation - Pose Estimation |
3 | 3 | CV Applications |
| Day 38-40 |
NLP Specialization - Text Classification - Named Entity Recognition - Language Models |
3 | 3 | Text Processing |
| Day 41-44 |
Model Deployment - TensorFlow Serving - ONNX Format - Cloud Deployment |
2 | 4 | Production Ready Models |
| Day 45-50 |
Final Project & Portfolio - End-to-End DL System - Model Optimization - Portfolio Development |
2 | 3 | Showcase Projects |
Key Recommendations
- Daily Practice: Implement models and experiment with different architectures daily
- Projects: Build at least 5 complete Deep Learning projects by the end
- Mathematics: Continuously strengthen your understanding of linear algebra and calculus
- Community: Join DL communities like PyTorch, TensorFlow, and Kaggle
- Stay Updated: Follow latest research papers and model architectures
- Ethics First: Always consider ethical implications of your AI applications
Comprehensive Deep Learning Learning Path
This Deep Learning roadmap on Nikhil Learn Hub provides a structured learning path: Learn deep learning concepts including neural networks, CNNs, RNNs, AI models, and practical implementation steps.
Use the schedule, weekly tables, and practice notes on this page to pace your progress. Keep the Deep learning cheatsheet open for syntax and API reminders during exercises.
Foundation phase
- Core concepts and terminology for this stack
- Guided exercises and small coding drills
- Hands-on labs aligned with each milestone
- Review checkpoints before moving forward
Advanced phase
- Multi-topic projects and integration tasks
- Performance, security, or scalability basics
- Tooling and workflow patterns used in industry
- Interview, certification, or portfolio preparation
Who Should Follow This Roadmap
Students, career switchers, and developers upskilling in Deep Learning can follow this roadmap for credible study order instead of scattered tutorials.
Related Resources on Nikhil Learn Hub
- Deep learning cheatsheetquick reference while you follow this roadmap
- Technology roadmaps hubbrowse all structured learning paths
- Technology hubbroader programming and AI resources