Understand the basic architecture and functioning of neural networks, including neurons, layers, activation functions, and backpropagation.
Gain proficiency in popular deep learning frameworks like TensorFlow and PyTorch, learning how to build, train, and deploy deep learning models efficiently.
Master CNNs for image recognition tasks, learning about convolutional layers, pooling layers, and techniques for improving model performance.
Explore RNNs for sequence data analysis, understanding concepts like long short-term memory (LSTM) and gated recurrent units (GRU) for tasks such as natural language processing and time series prediction.
Learn how to leverage pre-trained models and transfer learning techniques to accelerate model training and improve performance on tasks with limited data.
Dive into advanced topics such as generative adversarial networks (GANs), reinforcement learning, and attention mechanisms, enabling you to tackle complex deep learning problems and stay at the forefront of AI research.
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