- Understanding of Artificial Intelligence, Machine Learning, and Deep Learning
- Basics of Neural Networks
- Activation Functions (e.g., ReLU, Sigmoid, Tanh)
- Loss Functions (e.g., Mean Squared Error, Cross-Entropy)
- Optimization Algorithms (e.g., Gradient Descent, Adam)
- Overfitting and Regularization Techniques (e.g., Dropout, L1/L2 Regularization)
- Data Preprocessing and Augmentation
- Understanding of Hyperparameters and Their Tuning
- Model Evaluation Metrics (e.g., Accuracy, Precision, Recall)
Developed
- an Artificial Neural Network to predict the net hourly electrical output based on features like temperature, humidity, pressure, the exhaust vacuum, etc.
- an Artificial Neural Network to predict if a particular bank customer will churn from the bank.
Concepts Learned:
- Structure of ANNs (Neurons, Layers, Weights, Biases)
- Forward Propagation and Backpropagation
- Batch Normalization
- Weight Initialization Techniques
Developed a Dog vs Cat CNN Classifier. Built my own streamlit app
(Demo video available here here)
Concepts Learned:
- Understanding Convolutional Layers
- Pooling Layers (Max Pooling, Average Pooling)
- Stride and Padding Concepts
- Applications of CNNs (e.g., Image Recognition, Object Detection)
Developed an LSTM (Long Short-Term Memory) Recurrent Neural Network to predict stock prices
Concepts Learned:
- Structure and Functioning of RNNs
- Problems of Vanishing and Exploding Gradients
- Long Short-Term Memory (LSTM) Networks
- Applications of RNNs (e.g., Time Series Prediction, Natural Language Processing)