joelouismarino.github.io

Joe Marino

I am a Ph.D. student in the Computation and Neural Systems. Program at the California Institute of Technology. I am co-advised by Professors Pietro Perona. My focus is on deep learning, with the goal of extracting structure from data in unsupervised or semi-supervised settings. Non-expert Labels Improve Fine-Grained Object Recognition. Thoughts on Generative Models. A collection of my favorite blogs and podcasts on machine learning and deep learning. A brief guide on how to get started with deep learning.

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Joe Marino | joelouismarino.github.io Reviews

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I am a Ph.D. student in the Computation and Neural Systems. Program at the California Institute of Technology. I am co-advised by Professors Pietro Perona. My focus is on deep learning, with the goal of extracting structure from data in unsupervised or semi-supervised settings. Non-expert Labels Improve Fine-Grained Object Recognition. Thoughts on Generative Models. A collection of my favorite blogs and podcasts on machine learning and deep learning. A brief guide on how to get started with deep learning.

<META> KEYWORDS

1 joe marino

2 about me

3 and yisong yue

4 download cv

5 research

6 taxonomic curriculum learning

7 blog

8 googlenet in keras

9 implementing backpropagation

10 other resources

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joe marino,about me,and yisong yue,download cv,research,taxonomic curriculum learning,blog,googlenet in keras,implementing backpropagation,other resources,machine learning blogs,learning deep learning,twitter,github,linkedin,e mail

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Joe Marino | joelouismarino.github.io Reviews

https://joelouismarino.github.io

I am a Ph.D. student in the Computation and Neural Systems. Program at the California Institute of Technology. I am co-advised by Professors Pietro Perona. My focus is on deep learning, with the goal of extracting structure from data in unsupervised or semi-supervised settings. Non-expert Labels Improve Fine-Grained Object Recognition. Thoughts on Generative Models. A collection of my favorite blogs and podcasts on machine learning and deep learning. A brief guide on how to get started with deep learning.

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joelouismarino.github.io

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Machine Learning Blogs

http://joelouismarino.github.io/other_resources/ml_blogs.html

Talking Machines is a podcast by Ryan Adams and Katherine Gorman. In each episode, they review some interesting, typically new, ideas in machine learning, answer a listener question, and interview a prominent expert in machine learning. The podcast is a great way to learn about machine learning in a less technical setting. Shakir Mohamed's Blog: The Spectator. Chris Olah was awarded a Thiel Fellowship, which encourages its recipients to drop out of college to instead start companies or pursue research (i...

2

Blog - GoogLeNet in Keras

http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html

Joe Marino - June 2016. In this new era of deep learning, a number of software libraries have cropped up, each promising users speed, ease of use, and compatibility with state-of-the-art models and techniques. The go-to library in the Caltech vision lab has been Caffe. An open-source library developed by Yangquing Jia. And maintained by the Berkeley Vision and Learning Center (BVLC). A python library developed by François Chollet. That runs on top of Theano. Download the Keras Model. Before we begin, if ...

3

Learning Deep Learning

http://joelouismarino.github.io/other_resources/learning_deep_learning.html

4

Blog - Thoughts on Generative Models

http://joelouismarino.github.io/blog_posts/blog_VAE.html

Thoughts on Generative Models. Joe Marino - October 2016. Deep learning has exploded in recent years. Researchers are continually coming up with new and exciting ways to compose deep networks to perform new tasks and learn new things. If I can borrow Nando de Freitas'. The world’s tallest lego tower in Milan reaches a height of 35 meters. Neocortical anatomical structure. Left:. Six-layered architecture of neocortex, differentiated by neuron morphologies. Right:. Choosing a network architecture, 2).

5

Blog - Implementing Backpropagation

http://joelouismarino.github.io/blog_posts/blog_backprop.html

Joe Marino - March 2016. This past fall, I had the pleasure of TAing Caltech's course on neural computation, CNS 187. The course provides an overview of a number of biologically-inspired methods of computation, many of which are taken from the machine learning literature. The pinnacle of the class is the dreaded multilayer perceptron. A standard feedforward artificial neural network. Students are required to implement a general neural network, including the backpropagation. Of these two vectors: begin{eq...

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Computational Vision: [People]

http://vision.caltech.edu/people.html

Erge Belongie (Cornell Tech). Kristin Branson (HHMI Janelia Labs). Domitilla del Vecchio (MIT). Rob Fergus (NYU and Facebook). Thomas Fuchs (Memorial Sloan Kettering). Fei Fei Li (Stanford University). Michael Maire (Toyota Technical Institute Chicago). Merrielle Spain (MIT - Lincoln Labs). Marco Tartagni (U. Bologna). Max Welling (Univ of Amsterdam). Senior Scientist, Forza Silicon. Arrigo Benedetti : Senior Electrical Engineer, KLA-Tencor - Wafer Inspection Group. BlueMountain Capital Management,.

yisongyue.com

Yisong Yue | Group

http://www.yisongyue.com/group.php

Machine Learning Researcher @. Research Groups and Centers. Director) - statistical decision theory, machine learning, and optimization. Rigorous systems analysis and design. Computer vision, perception, and behavior studies. Interdisciplinary center for studying the mathematical foundations of information and computation and their interplay with science and engineering. Interdisciplinary center for studying autonomous systems. Computing and Mathematical Sciences. Computation and Neural Systems.

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joelouismarino.github.io

Joe Marino

I am a Ph.D. student in the Computation and Neural Systems. Program at the California Institute of Technology. I am co-advised by Professors Pietro Perona. My focus is on deep learning, with the goal of extracting structure from data in unsupervised or semi-supervised settings. Non-expert Labels Improve Fine-Grained Object Recognition. Thoughts on Generative Models. A collection of my favorite blogs and podcasts on machine learning and deep learning. A brief guide on how to get started with deep learning.

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