Deep Learning Course Syllabus — Complete Guide

Course Syllabus · Deep Learning

Deep Learning Course Syllabus — Complete Guide

AK Ankit Kaushik · Lead Trainer, Data Engineering & AI | Updated 11 November 2026 | 18 min read Free · Course Syllabus

The deep learning course builds from neural network basics to CNNs, RNNs, transformers, and real-world projects.

Quick summary — deep learning course syllabus

A complete deep learning course from fundamentals to transformers. This syllabus covers neural networks, CNNs, RNNs, transformers, and hands-on projects with TensorFlow and PyTorch.

In this syllabus you will learn:

Module 1: Neural Network Basics — perceptrons, activations, backprop.
Module 2: CNN & RNN — image and sequence models.
Module 3: Transformers — attention, LLMs, fine-tuning.
Hands-on labs — TensorFlow and PyTorch projects.
Capstone project — a real deep learning application.

SECTION 01Module 1: Neural Network Basics

This module introduces the building blocks of deep learning — how neural networks work and how they learn from data.

Topic	What You Learn	Labs
Perceptrons	Neurons, weights, bias, activation functions	Build a perceptron from scratch
Backpropagation	Gradients, chain rule, loss functions	Train a simple feedforward network
Optimizers	SGD, Adam, learning rate scheduling	Compare optimizers on MNIST
Regularization	Dropout, batch norm, early stopping	Add dropout to a model
Frameworks	TensorFlow and PyTorch basics	Build a model in both frameworks

Key insight: Module 1 is about understanding how a neural network learns. Do not skip the math — it makes debugging models much easier later.

SECTION 02Module 2: CNN & RNN

This module covers the two most important specialized architectures — CNNs for images and RNNs for sequences.

Topic	What You Learn	Labs
CNN Basics	Convolutions, pooling, stride, padding	Build a CNN for CIFAR-10
Transfer Learning	Pretrained models, fine-tuning	Fine-tune ResNet on custom data
RNN Basics	Sequences, hidden state, vanishing gradients	Build a sentiment classifier
LSTM & GRU	Gated architectures, long-term dependencies	Time-series forecasting with LSTM
Embeddings	Word vectors, embedding layers	Train word2vec on text data

Key insight: Module 2 is where most real-world deep learning happens. CNNs and RNNs power everything from medical imaging to speech recognition.

SECTION 03Module 3: Transformers

This module covers the architecture behind modern AI — transformers, attention, and large language models.

Topic	What You Learn	Labs
Attention	Self-attention, Q/K/V, positional encoding	Implement scaled dot-product attention
Transformer Block	Multi-head attention, feedforward, layer norm	Build a mini transformer
LLMs	GPT, BERT, tokenization, pretraining	Load and use a pretrained LLM
Fine-tuning	LoRA, PEFT, instruction tuning	Fine-tune a small model on custom data
Deployment	Model serving, quantization, inference	Deploy a model with Flask

Key insight: Transformers are the foundation of modern AI. Understanding attention is essential for anyone working with LLMs.

SECTION 04Hands-on labs

Every module includes hands-on labs. You will use both TensorFlow and PyTorch so you can work in any team.

import tensorflow as tf

# Build a CNN for image classification
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(32, 32, 3)),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Conv2D(64, 3, activation='relu'),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, validation_split=0.2)

import torch
import torch.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = self.dropout(torch.relu(self.fc1(x)))
        return self.fc2(x)

model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

deep-learning-labs.py

Pro tip: Learn both TensorFlow and PyTorch. TensorFlow is strong in production; PyTorch is popular in research. Knowing both makes you flexible.

SECTION 05Capstone project

The course ends with a capstone project where you build and deploy a real deep learning application:

Choose a domain — vision, text, or audio
Collect and prepare your dataset
Build and train a model (CNN, RNN, or Transformer)
Evaluate and improve performance
Deploy the model with Flask or FastAPI
Document the project for your portfolio

Key insight: The capstone project is your portfolio piece. A well-documented project with a live demo is worth more than a certificate alone.

SECTION 06Interview Q&A — deep learning syllabus

Q1Do I need math for deep learning?

Yes — basic linear algebra, calculus, and probability are essential. Module 1 covers the math you need in a practical way.

Q2Should I learn TensorFlow or PyTorch first?

Either is fine. TensorFlow with Keras is beginner-friendly; PyTorch is more flexible and popular in research. The course covers both.

Q3How long does the course take?

Most students complete the course in 4-6 months with 6-8 hours per week. The capstone project may add 2-3 weeks.

Q4Do I need a GPU?

A GPU speeds up training, but you can complete most labs with Google Colab (free GPU) or Kaggle. A local GPU is helpful but not required.

Q5What jobs can I apply for after this course?

You can apply for Machine Learning Engineer, Deep Learning Engineer, AI Research Assistant, and Computer Vision Engineer roles — depending on experience and portfolio.

SECTION 07Test yourself — deep learning quiz

Five questions. No sign-up.

0 / 5

Pick an answer to see why it is right or wrong.

SECTION 08Frequently asked questions

Is deep learning hard for beginners?

Deep learning is challenging but learnable. Start with Python and basic machine learning, then move to Module 1. Consistent practice is key.

Do I need machine learning experience first?

Yes — a basic understanding of machine learning (regression, classification, training/testing) makes deep learning much easier.

Which framework is more in demand?

Both are in demand. PyTorch is dominant in research and is gaining ground in industry. TensorFlow remains strong in production and enterprise.

Is deep learning still relevant in 2026?

Yes — deep learning is the foundation of modern AI, including LLMs, computer vision, and speech. Demand continues to grow.

Can I get a job with just this course?

The course gives you skills. A strong capstone project, portfolio, and some real-world practice greatly improve your chances. Apply for junior ML roles.

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Complete deep learning syllabus
TensorFlow & PyTorch labs
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Deep Learning Course Syllabus Complete Guide