Course Modules:
Module 1: Introduction to Deep Learning
What is deep learning and why does it matter?
Neural networks vs. traditional machine learning
Use cases in vision, NLP, and forecasting
Module 2: Anatomy of a Neural Network
Neurons, weights, biases, layers
Activation functions (ReLU, Sigmoid, Tanh)
Feedforward and backpropagation logic
Module 3: Training Neural Networks
Cost functions and optimization
Gradient descent and learning rate tuning
Overfitting, dropout, and regularization
Module 4: Convolutional Neural Networks (CNNs)
Filters, kernels, and convolution layers
Pooling, padding, and architecture stacking
Image classification and object recognition
Module 5: Recurrent Neural Networks (RNNs) and LSTMs
Sequence modeling and time-series prediction
Vanishing gradient problem and LSTM/GRU solutions
Applications in speech and text generation
Module 6: Transfer Learning and Pretrained Models
Fine-tuning and feature extraction
Using models like VGG, ResNet, and MobileNet
Faster training with fewer data
Module 7: Model Evaluation and Tuning
Validation, loss tracking, and early stopping
TensorBoard for model visualization
Hyperparameter search and scaling up
Module 8: Capstone Project
Choose one:
Image classifier with CNN
Time-series predictor with LSTM
Custom architecture using TensorFlow or PyTorch
Submit working model, documentation, and evaluation
Tools & Technologies Used:
TensorFlow and Keras
PyTorch
Jupyter Notebook / Google Colab
Optional: TensorBoard, Hugging Face models
Target Audience:
Intermediate learners in AI and ML
Python developers entering deep learning
Engineers building AI-driven applications
Students preparing for roles in data science and R&D
Global Learning Benefits:
Build state-of-the-art deep learning systems
Gain hands-on experience with leading AI frameworks
Train AI models for vision, text, and time-series data
Advance your career in artificial intelligence and data science
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