1
Neural Networks Basics
Perceptrons, activation functions, backpropagation
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2
Optimizers
Different types of optimizers
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3
Convolutional Neural Networks
Image processing and computer vision
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4
RNN & LSTM
Sequential data and time series
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5
Optimization Techniques
SGD, Adam, learning rate scheduling
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6
ReduceLROnPlateau
Understanding plateaus and learning rate reduction
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7
Reproducibility
From randomness to determinism in TensorFlow
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8
Transfer Learning
Pre-trained models and fine-tuning
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9
Optuna
Complete Guide to Hyperparameter Tuning with Optuna
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