📌 Overview: This course provides a comprehensive understanding of neural networks, covering everything from building models from scratch to advanced architectures like CNNs, RNNs, LSTMs, and GANs.
Module 1: Neural Networks from Scratch
🔹 Introduction to Neural Networks & Perceptrons
🔹 Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
🔹 Forward Propagation & Backpropagation
🔹 Optimization Techniques (Gradient Descent, Adam)
🔹 Hands-on: Build a Simple Neural Network from Scratch Using Python & NumPy
Module 2: Convolutional Neural Networks (CNNs)
🔹 Introduction to Image Processing with Deep Learning
🔹 Convolutional Layers, Pooling, and Feature Extraction
🔹 Popular CNN Architectures (LeNet, AlexNet, VGG, ResNet)
🔹 Data Augmentation & Transfer Learning in CNNs
🔹 Hands-on: Train a CNN for Image Classification
Module 3: Recurrent Neural Networks (RNNs) & LSTMs
🔹 Understanding Sequential Data & Why We Need RNNs
🔹 RNNs vs. Long Short-Term Memory Networks (LSTMs)
🔹 Gated Recurrent Units (GRUs) for Efficient Learning
🔹 Applications in Time Series Forecasting & Natural Language Processing
🔹 Hands-on: Train an RNN/LSTM for Text Generation or Sentiment Analysis
Module 4: Generative Adversarial Networks (GANs)
🔹 What Are GANs? Generator vs. Discriminator
🔹 How GANs Create Realistic Images & Videos
🔹 Types of GANs (DCGAN, CycleGAN, StyleGAN)
🔹 Applications of GANs (AI Art, Deepfakes, Data Augmentation)
🔹 Hands-on: Train a GAN to Generate New Images
Final Project: Build & Train a Deep Learning Model
🔹 Choose a Deep Learning Task (Image Classification, Text Generation, or GAN-based Model)
🔹 Implement & Train the Model Using TensorFlow/PyTorch
🔹 Fine-tune & Optimize Performance
🔹 Deploy the Model (Optional)
Instructor
