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Deep learning: neural networks
Master neural networks from their foundations: the neuron and the perceptron, activation functions, backpropagation and gradient descent. You will build and train dense, convolutional (CNN) and recurrent (LSTM) networks with Keras and PyTorch, applying regularization, dropout and transfer learning on GPU. It is the fourth course of the Professional Certificate in Data Science and the bridge between classical machine learning and deploying models to production.
10 lessons 14 hours 1000 XP
Start the courseSyllabus
Module 1 · Neural network fundamentals
- 1The artificial neuron and the perceptron45 min
- 2Activation functions45 min
- 3Forward pass and backpropagation50 min
Module 2 · Training and optimization
- 4Gradient descent and optimizers50 min
- 5Dense networks with Keras50 min
- 6Neural networks with PyTorch50 min
- 7Regularization and dropout45 min
Module 3 · Specialized architectures
- 8Convolutional neural networks (CNNs) for vision55 min
- 9RNNs and LSTMs for sequences55 min
- 10Transfer learning50 min