You can find more information about our Introduction to AI and Deep Learning Course by visiting the Course Website.
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Theory: Introduction to AI and Deep Learning
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Motivation: Foundations and Terminology of Deep Learning
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AI vs ML vs DL: A comparison
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Features and Weights
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Machine Learning Recap: Linear Regression, Logistic Regression
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Activation Functions.
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Hands-on
- Introduction to Python Programming
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Theory: Neural Networks
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Neural Networks
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Loss Functions
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Gradient Descent
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Feedforward and Backward Propagation
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Deep Learning Model training
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Hands-on
- Building Neural Networks
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Theory: Dataset, Regularization and Hyperparameter Tuning
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Dataset splitting and distribution
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Evaluation Metrics
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Bias vs Variance
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Regularization: L1/L2 regularization, Dropout, Early Stopping
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Optimization methods
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Hyperparameter Tuning
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Hands-on
- Tuning Neural Networks
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Theory: Convolutional Neural Networks
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ANN vs CNN
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Convolution, pooling, padding, striding
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Transfer Learning
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Applications of CNN
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Hands-on
- Image Classification using CNN
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Theory: Recurrent Neural Networks
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ANN vs RNN
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Sequential Processing with RNN
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Forward and Back Propagation
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Language Models
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LSTM and GRU
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RNN Applications
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Hands-on
- Text Classification using RNN