📌 Overview: This course dives into the core concepts of deep learning, helping learners understand neural networks, image processing with CNNs, time-series analysis with RNNs, and the power of transfer learning.
Module 1: Neural Networks Explained
🔹 What is a Neural Network?
🔹 Understanding Perceptrons & Activation Functions
🔹 Forward & Backpropagation in Deep Learning
🔹 Optimizers: Gradient Descent, Adam, and RMSprop
🔹 Hands-on: Build a Simple Neural Network Using TensorFlow/PyTorch
Module 2: Convolutional Neural Networks (CNNs) for Image Processing
🔹 Understanding Convolutions & Filters
🔹 How CNNs Recognize Features in Images
🔹 Popular CNN Architectures (LeNet, AlexNet, VGG, ResNet)
🔹 Data Augmentation & Image Classification
🔹 Hands-on: Build an Image Classifier with CNNs
Module 3: Recurrent Neural Networks (RNNs) for Time Series Data
🔹 Why Use RNNs?
🔹 Sequence Models & Long Short-Term Memory (LSTMs)
🔹 Gated Recurrent Units (GRUs)
🔹 Time Series Forecasting & NLP Applications
🔹 Hands-on: Train an RNN for Sentiment Analysis or Stock Prediction
Module 4: Transfer Learning and Pre-trained Models
🔹 What is Transfer Learning?
🔹 Using Pre-trained Models (VGG, ResNet, BERT, GPT)
🔹 Fine-tuning vs. Feature Extraction
🔹 Applications in Image & Text Processing
🔹 Hands-on: Fine-tune a Pre-trained Model for a Custom Task
Final Project: Train Your First Neural Network
🔹 Choose a real-world problem (Image Classification, NLP, or Time Series)
🔹 Build and Train a Deep Learning Model
🔹 Evaluate and Improve Model Performance
🔹 Deploy the Model (Optional)
Instructor
