THE CHANNEL AI NOTEBOOK
Practical explanations, experiments, and lessons from building. Browse articles on deep learning, computer vision, and virtual reality, or search for a topic you want to understand.
Some articles are from the earlier Channel AI archive. Check publication dates and current library documentation when following older code examples.
Most of the times, when attempting to visualize and build deep learning models using real-world data, you usually never get a dataset fiiting your requirement closely. In this case, it be...
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Have you been feeling frustared about the complexity of deep learning terms like Convolutional Neural networks and Recurrent NNs?. So even if you have found it difficult to assimilate, I ...
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Are there specific skills important in deep learning?. You shouldn't be surprised if you ask yourself this question. At a time or another, all budding deep learner ask themselves the same...
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Can you imagine navigating through an unknown city without Google Maps?It is usually a tough and tiring ordeal, all paths seems to lead to nothing and at the end you even ask yourself why...
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Buiding Models is exciting, putting them to use can be very brain-tasking. For people who have mastered model deployment, usually this involves just a few tweaks to the code to make it wo...
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No doubt, AI is one of the most crucial future technologies which is being harnessed today and transforming our daily lives. A very important branch of Artificial Intelligence "Deep Learn...
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Virtual Reality is a technology that is relatively new in the industry, there exists some skeptisism about it but if we really use our imagination as is required, what does the future hol...
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In this tutorial you would see how to apply state-of-the-art R-CNN architecture model built on the MS Coco dataset for Object Detection on a new Dataset. We use Transfer Learning for effi...
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In this tutorial we would explore a necessary topic in deep learning - overfitting. We would learn what it is ,and how to combact it using known regularization methods like dropouts, batc...
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The task of helping computers to see, and make a sense or add meaning to what they see is a very hard task. Mainly because it does not just involve one but a series of steps are involved ...
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Using generative models, we first learn the distribution of the training set and then generate some new observations or data points using the learned distribution with some differences.
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Vectorization is the process of converting an algorithm from operating on a single value at a time to operating on a set of values (vector) at once.. In a vectorized calculation, all elem...
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Linear Regression is a Machine Learning algorithm that is used for predictions of "continuous /non-classified data".You have some datasets in continuous values(because we are doing Linear...
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Machine Learning is a sub-field of Artificial Intelligence. It is the concept that defines the different methods in which a Machine (preferably referred to as model) can learn from real w...
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When trying to build a high-brew model in image classification, you can solve the problem using different methods, but just few are efficient ways of doing so. In this tutorial we look at...
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