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Deep Learning Specialization on Coursera

A series of online courses offered by deeplearning.ai. Directed by Prof. Andrew Ng and his colleagues.

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Course Description

1. Neural Network and Deep Learning

Foundations of Deep Learning:

• Understand the major technology trends driving Deep Learning
• Be able to build, train and apply fully connected deep neural networks
• Know how to implement efficient (vectorized) neural networks
• Understand the key parameters in a neural network's architecture

Code:

• Logistic Regression as Neural Network
• Classification with one hidden layer
• Building Deep Neural Network
• Deep Neural Network Application for Image Classification

2. Improving Deep Neural Networks-Hyperparameter tuning, Regularization and Optimization

• Understand industry best-practices for building deep learning applications.
• Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
• Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
• Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
• Be able to implement a neural network in TensorFlow.

Code:

• Initialization
• Regularization
• Gradient Checking
• Optimization Methods
• Tensorflow HandSign

3. Convolutional Neural Network

• Understand how to build a convolutional neural network, including recent variations such as residual networks.
• Know how to apply convolutional networks to visual detection and recognition tasks.
• Know to use neural style transfer to generate art.
• Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

Code:

• Convolutional Model
• Keras Happy Model
• ResNets
• Car detection for Autonomous Driving
• Face Recognition
• Neural Style Transfer

4. Sequence Models

• Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• Be able to apply sequence models to natural language problems, including text synthesis.
• Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

Code:

• Building Reccurent Neural Network
• Dinosaur Island - Character level language
• Jazz Improvisation with LSTM
• Word Vector Representation
• Emojify
• Machine Translation
• Trigger Word Detection