• History of AI & Rise of Deep Learning
• Deep Learning vs Traditional ML
• What are Neural Networks? The Neuron (Perceptron)
• Activation Functions (Sigmoid, ReLU, Leaky ReLU, Tanh, Softmax)
• Introduction to TensorFlow/Keras and PyTorch (Basics: Tensors, Gradients)
• Setting up your DL environment (GPU considerations)
• Basics of Google Colab / Jupyter Notebooks for DL

• Multi-Layer Perceptrons (MLPs) Architecture
• Forward Propagation (Calculating Output)
• Loss Functions (MSE, Cross-Entropy)
• Optimizers (Gradient Descent, SGD, Adam, RMSprop)
• Backpropagation Algorithm (Conceptual Understanding)
• Training Process: Epochs, Batches, Iterations
• Implementing Simple ANNs in Keras/PyTorch
• Evaluating ANN Performance (Metrics)
• Introduction to Regularization (L1, L2)

• Introduction to Computer Vision
• Convolutional Layer (Filters, Strides, Padding)
• Pooling Layer (MaxPooling, AveragePooling)
• Activation Layers & Dropout
• Building CNN Architectures (Lenet, AlexNet concepts)
• Working with Image Data (Loading, Augmentation)
• Implementing CNNs in Keras/PyTorch for Image Classification
• Transfer Learning: Using Pre-trained Models (VGG, ResNet concepts)
• Fine-tuning Pre-trained Models

• Introduction to Sequence Data (Time Series, Text)
• Recurrent Neural Networks (RNNs) Architecture
• Challenges with RNNs (Vanishing/Exploding Gradients)
• Long Short-Term Memory (LSTM) Networks
• Gated Recurrent Units (GRUs)
• Working with Text Data (Tokenization, Padding)
• Word Embeddings (Word2Vec, GloVe concepts)
• Implementing RNN/LSTM/GRU for Sequence Classification (e.g., Sentiment Analysis)
• Basic Time Series Forecasting with RNNs

• Advanced Regularization Techniques (Batch Normalization, Dropout Explained)
• Callbacks (Early Stopping, Learning Rate Scheduling)
• Introduction to Attention Mechanisms
• Introduction to Transformer Networks (Conceptual Overview)
• Generative Models Introduction (VAEs, GANs concepts - Optional)
• Model Ensembling in Deep Learning
• Multi-input/Multi-output Models (Concepts)

• Saving and Loading Deep Learning Models
• Introduction to Model Deployment Considerations
• Building a Simple REST API for Model Inference (using Flask - Basic)
• Containerization Concepts (Docker - Optional)
• Deep Learning Capstone Project: End-to-End Implementation on a Complex Dataset
• Presenting Your Project
• Career Paths in Deep Learning & Portfolio Building

This course is suitable for individuals who have a solid foundation in Python programming and a basic understanding of machine learning concepts and related math (linear algebra, calculus, statistics). It's ideal for aspiring AI/ML engineers, data scientists, or researchers who want to specialize in neural networks and complex data types.

Strong Python programming skills are essential. Familiarity with basic machine learning concepts and libraries (like Scikit-learn) and a good grasp of fundamental math (linear algebra, calculus, probability, statistics) are highly recommended. This is not an introductory course to programming or basic ML.

The primary focus will be on TensorFlow and Keras, which are widely used in the industry. We will also provide an introduction to PyTorch, another popular framework, to give you versatility.

Absolutely. The course includes hands-on labs throughout and culminates in a comprehensive capstone project where you will apply your Deep Learning skills to a real-world problem involving complex data like images or text.

Deep Learning is a subset of Machine Learning that uses neural networks with multiple layers (hence "deep") to automatically learn complex patterns directly from raw data. It excels at tasks involving unstructured data like images, audio, and text, where traditional ML methods often require significant manual feature engineering.