Build a solid foundation in deep learning using the Keras API. This course guides you through essential neural network concepts, practical implementation techniques, and building standard models like CNNs and RNNs for common AI tasks. Learn to train, evaluate, and improve your deep learning models effectively with Python and Keras.
Prerequisites Python, basic ML concepts
Level:
Neural Network Concepts
Understand the structure and function of artificial neural networks, including layers, activation functions, and weights.
Keras API Proficiency
Become proficient in using the Keras API (Sequential and Functional) to define, build, and compile deep learning models.
Model Training Process
Grasp the mechanics of training deep learning models, including loss functions, optimization algorithms, and backpropagation.
Convolutional Neural Networks (CNNs)
Implement CNNs using Keras for image recognition tasks, understanding convolutional and pooling layers.
Recurrent Neural Networks (RNNs)
Implement RNNs and LSTMs using Keras for sequence data processing tasks like text analysis.
Model Evaluation and Improvement
Learn techniques to evaluate model performance, prevent overfitting, and apply methods like regularization and callbacks.
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