Overview of deep learning, its applications, and the role of TensorFlow as a leading framework.

Understand the foundational concepts of neural networks, including neurons, layers, and activation functions.

Guide to installing TensorFlow and configuring your development environment for deep learning projects.

Build and train your first simple neural network in TensorFlow to grasp the basic workflow.

Explore TensorFlow’s core components, data structures (tensors), and computational graph concepts.

Understand the architecture and principles of various deep neural network types, such as CNNs and RNNs.

Learn to identify and mitigate overfitting in neural networks using techniques like regularization and dropout.

Discover different weight initialization strategies for neural networks and their impact on training stability and performance.

Explore various optimization algorithms (e.g., SGD, Adam, RMSprop) that accelerate neural network training and improve convergence.

Master essential data preprocessing techniques for deep learning, including normalization, standardization, and data augmentation.

Work through a more complex deep learning example, applying multiple concepts learned throughout the course.

Analyze a real-world business problem and develop a deep learning solution, demonstrating practical application of your skills.

Review key concepts and discuss next steps for advancing your deep learning expertise and career.