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Transfer Learning – Machine Learning’s Next Frontier
Table of contents: What is Transfer Learning? Why Transfer Learning Now? A Definition of Transfer Learning Transfer Learning Scenarios Applications of Transfer Learning Learning from simulations Adapting to new domains Transferring knowledge across languages Transfer Learning Methods Using pre-trained CNN features Learning domain-invariant representations Making representations more similar Confusing... Read more
Cognitive Machine Learning (1): Learning to Explain
Above is an image of the Zaamenkomst panel: one of the best remaining exemplars of rock art from the San people of Southern Africa. As soon as you see it, you are inevitably herded, like the eland in the scene, through a series of thoughts. Does it have a meaning?  Why are the eland running?... Read more
Faster deep learning with GPUs and Theano
Originally posted by Manojit Nandi, Data Scientist at STEALTHbits Technologies on the Domino data science blog Domino recently added support for GPU instances. To celebrate this release, I will show you how to: Configure the Python library Theano to use the GPU for computation. Build and train neural networks... Read more
On word embeddings – Part 1
Table of contents: A brief history of word embeddings Word embedding models A note on language modelling Classic neural language model C&W model Word2Vec CBOW Skip-gram Unsupervisedly learned word embeddings have been exceptionally successful in many NLP tasks and are frequently seen as something akin to a silver bullet.... Read more
Dropout with Theano
Almost everyone working with Deep Learning would have heard a smattering about Dropout. Albiet a simple concept (introduced a couple of years ago), which sounds like a pretty obvious way for model averaging, further resulting into a more generalized and regularized Neural Net; still when you actually get into... Read more
Background – How many cats does it take to identify a Cat? In this article, I cover the 12 types of AI problems i.e. I address the question : in which scenarios should you use Artificial Intelligence (AI)?  We cover this space in the  Enterprise AI course Some background: Recently, I conducted... Read more
ConceptNet 5.5 and conceptnet.io
ConceptNet is a large, multilingual knowledge graph about what words mean. This is background knowledge that’s very important in NLP and machine learning, and it remains relevant in a time when the typical thing to do is to shove a terabyte or so of text through a neural net.... Read more
HACKER’S GUIDE TO NEURAL NETWORKS, #2
Chapter 2: Machine Learning In the last chapter we were concerned with real-valued circuits that computed possibly complex expressions of their inputs (the forward pass), and also we could compute the gradients of these expressions on the original inputs (backward pass). In this chapter we will see how useful... Read more
TensorFlow and Queues
There are many ways to implement queue data structures, and TensorFlow has some of its own. FIFO Queue with a list In Python, a list can implement a first-in first-out (FIFO) queue, with slightly awkward syntax: >>> my_list = >>> my_list.insert(0, 'a') >>> my_list.insert(0, 'b') >>> my_list.insert(0, 'c')... Read more
Algorithms are Black Boxes, That is Why We Need Explainable AI
Artificial Intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the beginning of the AI revolution that is upon us, we can already see... Read more