Course can be found here
Notebook for quick search can be found here
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Week 1 Introduction to optimization
- Train a linear model for classification or regression task using stochastic gradient descent
- Tune SGD optimization using different techniques
- Apply regularization to train better models
- Use linear models for classification and regression tasks
- Linear models and optimization
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Week 2 Introduction to neural networks
- Explain the mechanics of basic building blocks for neural networks
- Apply backpropagation algorithm to train deep neural networks using automatic differentiation
- Implement, train and test neural networks using TensorFlow and Keras
- Going deeper with Tensorflow
- my1stNN
- Getting deeper with Keras
- Your very own neural network
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Week 3 Deep Learning for images
- Define and train a CNN from scratch
- Understand building blocks and training tricks of modern CNNs
- Use pre-trained CNN to solve a new task
- Your first CNN on CIFAR-10
- Week 3 PA 2 Fine-tuning InceptionV3 for flowers classification
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Week 4 Unsupervised representation learning
- Understand what is unsupervised learning and how you can benifit from it
- Implement and train deep autoencoders
- Apply autoencoders for image retrieval and image morphing
- Implement and train generative adversarial networks
- Understand basics of unsupervised learning of word embeddings
- Autoencoders
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Week 5 Deep learning for sequences
- Define and train an RNN from scratch
- Understand modern architectures of RNNs: LSTM, GRU
- Use RNNs for different types of tasks: sequential input, sequential output, sequential input and output
- Generating names with RNNs
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Week 6 Final Project