Global Certificate in Deep Learning for the Modern Workplace
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⢠Introduction to Deep Learning: Basics of neural networks, activation functions, backpropagation, and optimization algorithms.
⢠Convolutional Neural Networks (CNNs): Understanding CNN architecture, pooling, padding, and strides. Use cases in image recognition and computer vision.
⢠Recurrent Neural Networks (RNNs): Introduction to sequence data and time-series analysis. Long short-term memory (LSTM) and gated recurrent units (GRUs).
⢠Deep Learning Frameworks: Hands-on experience with popular deep learning frameworks, e.g., TensorFlow, Keras, PyTorch, and Theano.
⢠Natural Language Processing (NLP): Text preprocessing techniques, word embeddings, and applications of deep learning in NLP.
⢠Generative Adversarial Networks (GANs): Introduction to GANs, generator and discriminator networks, and their applications in image synthesis.
⢠Deep Reinforcement Learning (DRL): Multi-agent environments, Q-learning, Deep Q-Networks (DQNs), and policy gradients.
⢠Transfer Learning and Fine-Tuning: Leveraging pre-trained models, transfer learning, and fine-tuning in deep learning projects.
⢠Ethics and Bias in Deep Learning: Understanding ethical implications, detecting and mitigating model bias, and maintaining fairness in AI systems.
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