Eddy Shyu

Eddy is a product lead at deeplearning.ai, partnering with AI experts to build courses. Eddy was previously a curriculum developer at Udacity, and was co-instructor for the AI for Trading Nanodegree.

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Unsupervised Learning, Recommenders, Reinforcement Learning (Coursera)

Mar 25th 2024
Unsupervised Learning, Recommenders, Reinforcement Learning (Coursera)
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In the third course of the Machine Learning Specialization, you will: Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection; Build recommender systems with a collaborative filtering approach and a content-based deep learning method; Build a deep reinforcement learning [...]

Supervised Machine Learning: Regression and Classification (Coursera)

Mar 25th 2024
Supervised Machine Learning: Regression and Classification (Coursera)
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In the first course of the Machine Learning Specialization, you will: build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn; build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic [...]

Advanced Learning Algorithms (Coursera)

Mar 25th 2024
Advanced Learning Algorithms (Coursera)
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In the second course of the Machine Learning Specialization, you will: build and train a neural network with TensorFlow to perform multi-class classification; apply best practices for machine learning development so that your models generalize to data and tasks in the real world; build and use decision trees and [...]

Generative Deep Learning with TensorFlow (Coursera)

Mar 25th 2024
Generative Deep Learning with TensorFlow (Coursera)
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In this course, you will: a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image; b) Build simple AutoEncoders on the familiar MNIST [...]

Custom Models, Layers, and Loss Functions with TensorFlow (Coursera)

Mar 25th 2024
Custom Models, Layers, and Loss Functions with TensorFlow (Coursera)
Course Auditing
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In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network; • Build custom loss functions (including the contrastive loss function used in a Siamese network) in [...]

Custom and Distributed Training with TensorFlow (Coursera)

Mar 25th 2024
Custom and Distributed Training with TensorFlow (Coursera)
Course Auditing
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Effort
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In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients; • Build your own custom training loops using GradientTape and TensorFlow [...]

Natural Language Processing with Sequence Models (Coursera)

In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to [...]

Natural Language Processing with Classification and Vector Spaces (Coursera)

In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize [...]

Natural Language Processing with Probabilistic Models (Coursera)

In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming; b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics, c) Write a better auto-complete algorithm using [...]