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A Beginner’s Guide To Understanding Convolutional Neural Networks
Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to... Read more
Deciphering the Neural Language Model
Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student... Read more
Handwritten digits recognition using Tensorflow with Python
The progress in technology that has happened over the last 10 years is unbelievable. Every corner of the world is using the top most technologies to improve existing products while also conducting immense research into inventing products that make the world the best place to live.... Read more
Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data
Thomas originally posted this article here at http://twiecki.github.io  Most problems solved by Deep Learning are stationary. A cat is always a cat. The rules of Go have remained stable for 2,500 years, and will likely stay that way. However, what if the world around you is changing? This... Read more
TensorFlow Clusters: Questions and Code
One way to think about TensorFlow is as a framework for distributed computing. I’ve suggested that TensorFlow is a distributed virtual machine. As such, it offers a lot of flexibility. TensorFlow also suggests some conventions that make writing programs for distributed computation tractable. When is there... Read more
In this interview, Jonathan Schwarz of Google DeepMind shares insight on Deep Learning projects. He offers tips and advice for the those interested in DL, and explains whether DL projects relate to other data driven projects? He comments on effective team size, software, frameworks, common mistakes, resources for learning,... Read more
On word embeddings – Part 2: Approximating the Softmax
Table of contents: Softmax-based Approaches Hierarchical Softmax Differentiated Softmax CNN-Softmax Sampling-based Approaches Importance Sampling Adaptive Importance Sampling Target Sampling Noise Contrastive Estimation Negative Sampling Self-Normalisation Infrequent Normalisation Other Approaches Which Approach to Choose? Conclusion This is the second post in a series on word embeddings and... Read more
TensorFlow as a Distributed Virtual Machine
TensorFlow has a flexible API, and it has automatic differentiation, and it can run on GPUs. But the thing that’s really neat about TensorFlow is that it gives you a fairly general way to easily program across multiple computers. TensorFlow’s distributed runtime, the big bottom box... Read more
How NOT to program the TensorFlow Graph
Using TensorFlow from Python is like using Python to program another computer. Some Python statements build your TensorFlow program, some Python statements execute that program, and of course some Python statements aren’t involved with TensorFlow at all. Being thoughtful about the graphs you construct can help... Read more
Deep Learning as the apotheosis of Test-Driven Development
Even if you aren’t interested in data science, Deep Learning is an interesting programming paradigm; you can see it as “doing test-driven development with a ludicrously large number of tests, an IDE that writes most of the code, and a forgiving client.” No wonder everybody’s pouring... Read more