Live Course Module: Deep Learning Course for Data Science

Total Duration: 36 Hours (6 Weeks)

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Week 1: Introduction to Deep Learning (6 hrs)

Objective: Build foundational understanding of neural networks and their role in modern data science.
Topics Covered:

1. What is Deep Learning and how it differs from Machine Learning

2. Key Concepts: Neurons, Layers, Activation Functions

3. Biological vs Artificial Neural Networks

4. Deep Learning in Data Science Applications (vision, NLP, recommender systems)

5. Setting up the Environment – TensorFlow, Keras, and PyTorch basics

6. Hands-on: Build your first Neural Network using Keras

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🗓️ Week 2: Artificial Neural Networks (ANN) (6 hrs)

Objective: Develop a strong understanding of feedforward and backpropagation algorithms.
Topics Covered:

1. Architecture of ANN: Input, Hidden, Output Layers

2. Forward Propagation and Backpropagation

3. Gradient Descent and Optimization Techniques (SGD, Adam, RMSProp)

4. Loss Functions and Evaluation Metrics

5. Overfitting & Underfitting, Regularization (Dropout, Batch Normalization)

6. Hands-on: Predicting customer churn using ANN

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🗓️ Week 3: Convolutional Neural Networks (CNN) (6 hrs)

Objective: Learn how to process and analyze image data using CNNs.
Topics Covered:

1. Concept of Convolution, Filters, Pooling, and Feature Maps

2. CNN Architectures – LeNet, AlexNet, VGG, ResNet

3. Data Augmentation and Transfer Learning

4. Hyperparameter Tuning in CNNs

5. Real-world Applications – Image Classification, Object Detection

6. Hands-on: Build an image classifier using CNN in TensorFlow

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🗓️ Week 4: Recurrent Neural Networks (RNN) & LSTM (6 hrs)

Objective: Master deep learning for sequential and time-series data.
Topics Covered:

1. Introduction to Sequential Data

2. RNN Architecture and Vanishing Gradient Problem

3. Long Short-Term Memory (LSTM) and GRU Networks

4. Applications – Stock Prediction, Text Generation, Sentiment Analysis

5. Sequence-to-Sequence Models

6. Hands-on: Sentiment analysis using LSTM on IMDB dataset

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🗓️ Week 5: Advanced Architectures & NLP (6 hrs)

Objective: Explore transformers, attention mechanisms, and advanced NLP techniques.
Topics Covered:

1. Understanding Attention Mechanism

2. Transformer Architecture – Encoder & Decoder

3. Introduction to BERT, GPT Models

4. Word Embeddings: Word2Vec, GloVe, FastText

5. NLP Applications: Text Classification, Named Entity Recognition

6. Hands-on: Build a text classifier using BERT

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🗓️ Week 6: Generative Models & Capstone Project (6 hrs)

Objective: Implement generative and hybrid models and complete an end-to-end project.
Topics Covered:

1. Autoencoders & Variational Autoencoders (VAE)

2. Generative Adversarial Networks (GANs) and their Applications

3. Deep Reinforcement Learning Overview

4. Model Deployment (Flask/Streamlit/TensorFlow Serving)

5. Capstone Project: Choose one –

   • Image Caption Generator

   • Fake News Detector

   • GAN-based Image Generator

6. Presentation & Review

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🎯 Course Outcomes

By the end of this course, learners will be able to:

• Build, train, and optimize deep learning models using TensorFlow and PyTorch

• Apply CNNs and RNNs for image, text, and sequence data

• Understand and implement transformer-based models like BERT and GPT

• Deploy deep learning models into production environments

• Complete a full deep learning project for real-world data science applications

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🧰 Tools & Technologies Used

• Programming: Python

• Frameworks: TensorFlow, Keras, PyTorch

• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, OpenCV

• Deployment: Flask / Streamlit

• Datasets: CIFAR-10, MNIST, IMDB, Custom Dataset