xincxiong / Deep-Learning-Specialization-Notes

Andrew NG - deeplearning.ai- Certificate. this repo includes all programming assignments.

DeepLearning_ai by Andrew NG

This repo includes all programming assignments from deeplearning specilization supported by Andrew Ng on Coursera. I have completed these five courses and got the certificate on Feb, 2018. Link:deeplearning.ai Certificate Link:deeplearning

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Author: Xin Xiong

This specialization includes the foundations of Deep Learning understand how to build neural network, and learn how to lead successful machine learning projects. I have learn about Convolutional Networks,RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. I have worked on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. I mastered not only the theory, but also see how it is applied in industry. I practiced all these ideas in Python and in TensorFlow and Keras.

1. Neural Networks and 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

2. Improving Deep Neural Networks Hyperparameter

   • 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 and Keras.

3. Structuring Machine Learning Projects

   • Understand how to diagnose errors in a machine learning system, and
   • Be able to prioritize the most promising directions for reducing error
   • Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
   • Know how to apply end-to-end learning, transfer learning, and multi-task learning

4. Convolutional Neural Networks

   • 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.

5. 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.

If you have any questions, please drop me an email at: [e-mail]