Build a solid foundation in deep learning concepts and techniques. This course covers neural network fundamentals, essential algorithms like backpropagation and gradient descent, and practical implementation using modern libraries. Gain the skills to build and train foundational deep learning models.
Prerequisites Python programming, basic math
Level:
Neural Network Fundamentals
Explain the structure and components of artificial neural networks, including neurons, layers, weights, and biases.
Activation Functions
Understand the purpose and characteristics of common activation functions like Sigmoid, Tanh, ReLU, and Leaky ReLU.
Model Training
Describe the process of training a neural network, including loss functions, gradient descent, and backpropagation.
Optimization Algorithms
Compare different optimization algorithms like SGD, Momentum, RMSprop, and Adam.
Regularization Techniques
Apply techniques like L1/L2 regularization and dropout to prevent overfitting.
Building Feedforward Networks
Implement and train basic feedforward neural networks using a common deep learning framework (e.g., TensorFlow/Keras or PyTorch).
Introduction to CNNs and RNNs
Recognize the basic structure and purpose of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
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