Deep Learning Full Syllabus
Duration: 4–6 Months (Ideal Pace)
Prerequisites: Python basics, Linear Algebra, Basic Calculus, Probability, and Machine Learning foundations
Module 1: Getting Started with Deep Learning
Understand what deep learning really is, how it evolved, and where it’s being used today.
Topics:
- What is Deep Learning?
- Difference between AI, ML, and DL
- History and Evolution of Neural Networks
- Real-world Applications (Self-driving cars, Face Recognition, etc.)
- Overview of Deep Learning Tools: TensorFlow, Keras, PyTorch
Project Idea: Analyze how Netflix recommends movies using a basic similarity engine.
Module 2: Math for Deep Learning (No Stress Approach)
You don’t need to be a math wizard, but you do need to get comfortable with some core ideas.
Topics:
- Vectors, Matrices, and Tensors
- Matrix Multiplication and Dot Products
- Activation Functions (ReLU, Sigmoid, Tanh)
- Gradients and Derivatives (Basic Intuition)
- Backpropagation in Simple Terms
Practice: Use NumPy to simulate small matrix operations and learn how data flows in layers
Module 3: Neural Networks Basics
Learn how the brain of deep learning (neural networks) works under the hood.
Topics:
- What is a Neuron?
- Input Layer, Hidden Layers, Output Layer
- Forward and Backward Propagation (Simplified)
- Loss Functions and Optimizers
- Overfitting, Underfitting, and Regularization
Project: Build a neural network from scratch (without libraries) to recognize digits (MNIST dataset).
Module 4: Deep Neural Networks with TensorFlow or PyTorch
Get hands-on with real frameworks that companies use.
Topics:
- Installing TensorFlow/PyTorch
- Layers, Models, and Activation Functions
- Training, Validation, Testing
- Epochs, Batches, and Iterations
- Save and Load Models
Project: Build a binary image classifier (cats vs dogs).
Module 5: Convolutional Neural Networks (CNNs)
Teach machines how to see like humans do.
Topics:
- What are Convolutions and Kernels?
- Pooling Layers (MaxPooling, AveragePooling)
- Flattening and Fully Connected Layers
- Use Cases: Image Classification, Object Detection
Project: Build an emotion recognition model from facial expressions.
Module 6: Recurrent Neural Networks (RNNs) & LSTMs
Understand time-series and sequence data like text or stock prices.
Topics:
- What is a Recurrent Neural Network?
- Vanishing Gradient Problem
- Long Short-Term Memory (LSTM) Units
- Applications: Chatbots, Music Generation, Time Series Forecasting
Project: Predict stock price movement or next word in a sentence.
Module 7: Natural Language Processing (NLP) with Deep Learning
Let your models understand and generate human language.
Topics:
- Word Embeddings (Word2Vec, GloVe)
- Tokenization, Padding, and Text Preprocessing
- Sentiment Analysis
- Transformers (Intro)
Project: Build a sentiment analyzer for movie reviews.
Module 8: Generative Models (GANs)
Create new data from nothing — like fake images, music, or voices.
Topics:
- What is a GAN (Generative Adversarial Network)?
- Generator vs Discriminator
- GAN Training Process
- Deepfakes, Art Generation, Synthetic Data
Project: Train a GAN to generate new handwritten digits or art.
Module 9: Model Optimization & Deployment
Make your models faster, lighter, and ready for the real world.
Topics:
- Hyperparameter Tuning
- Transfer Learning
- Quantization, Pruning, and Optimization
- Exporting Models (ONNX, SavedModel)
- Model Deployment (Flask, FastAPI, Streamlit)
Project: Deploy a deep learning app that classifies plant diseases.
Module 10: Capstone Project – Build Something Real
Combine everything and solve a problem that matters to you.
Capstone Steps:
- Pick a problem (e.g., pneumonia detection from chest X-rays)
- Collect or find dataset
- Design the neural network
- Train and fine-tune your model
- Evaluate and deploy your solution
Tools You’ll Learn:
- Programming: Python
- Libraries: NumPy, Pandas, Matplotlib
- Frameworks: TensorFlow, Keras, PyTorch
- Platforms: Google Colab, Kaggle, Hugging Face
Outcomes:
- Build and train deep neural networks
- Apply CNNs for image tasks, RNNs for time-series/text
- Use real-world data to solve real problems
- Deploy your models like a pro
