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Automatic Differentiation (AD) is a vital process in Deep Learning. Many of deep learning’s techniques like backpropagation relies heavily on AD. There are multiple ways to implement AD, one of which is utilizing Dual Numbers.
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On June 23, the HuggingFace team announced that they are planning to host a community week together with the people from the Google Cloud team. The main gist of this event was getting everyone to learn and use HuggingFace’s newly integrated JAX framework. But aside from just learning from tutorials, we were equipped with blazing fast TPUs thanks to the amazing Google Cloud team 🤯.
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The dataset used for this task if from a Kaggle dataset by Paul Mooney. It consists of two kinds of chest x-rays, those infected by pneumonia, and the other being normal. Our main goal is to distinguish which chest corresponds to pneumonia-infected ones and which aren’t. Note that the dataset is highly imbalanced, like many medical image dataset are.
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Aksara Jawa, or the Javanese Script is the core of writing the Javanese language and has influenced various other regional languages such as Sundanese, Madurese, etc. The script is now rarely used on a daily basis, but is sometimes taught in local schools in certain provinces of Indonesia.
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According to Wikipedia, a hash table or sometimes called hash map is a a data structure that implements an associative array abstract data type, a structure that can map keys to values.
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After learning how to implement Singly Linked List, we’re going to implement Doubly Linked List, which is similar to Singly Linked List, but with the addition of a prev pointer which points to the node before it.
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According to Wikipedia, a linked list is a linear collection of data elements, whose order is not given by their physical placement in memory. Instead, each element points to the next. It is a data structure consisting of a collection of nodes which together represent a sequence.
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Fast.ai has a two-part Deep Learning Course, the first being Practical Deep Learning for Coders, and the second being Deep Learning from the Foundations, both having different approaches and intended for different audiences. In the 7th lecture of Part 1, Jeremy Howard taught a lot about modern architectures such as Residual Network (ResNet) , U-Net, and Generative Adversarial Network (GAN).
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Optimization is a key process in machine learning, from which we can approach inference and learning. It allows us to decouple the mathematical specification of what we want to compute from the algorithms for how to compute it.
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Tensorflow is one of the most used deep learning frameworks today, bundled with many features for end-to-end deep learning processes. Recently, they have just announced a new library on top of Tensorflow, called Tensorflow Quantum. Tensorflow Quantum integrates with Cirq, which provides quantum computing algorithms, and the two works well to do tasks involving Quantum Machine Learning.
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Qiskit is IBM’s open-source framework to do quantum processes which provides users access to both simulators and real Quantum Computers. Today, the Quantum Computer available is still in the Noisy Intermediate-Scale Quantum (NISQ) era and is very much sensitive to any forms of interference. Unlike real Quantum Computers, simulators provided by Qiskit aren’t noisy and is great for prototyping.
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Tensorflow is one of the most used deep learning frameworks today, bundled with many features for end-to-end deep learning processes. Recently, they have just announced a new library on top of Tensorflow, called Tensorflow Quantum. Tensorflow Quantum integrates with Cirq, which provides quantum computing algorithms, and the two works well to do tasks involving Quantum Machine Learning.
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Qiskit is IBM’s open-source framework to do quantum processes which provides users access to both simulators and real Quantum Computers. Today, the Quantum Computer available is still in the Noisy Intermediate-Scale Quantum (NISQ) era and is very much sensitive to any forms of interference. Unlike real Quantum Computers, simulators provided by Qiskit aren’t noisy and is great for prototyping.
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Our team at Bookbot is currently developing a grapheme-to-phoneme Python package for Bahasa Indonesia. The package is highly inspired by its English counterpart, g2p. A lot of our design and methods are borrowed from that library, most notably the steps to predict phonemes. The English g2p used the following algorithm (c.f. g2p’s README):
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On June 23, the HuggingFace team announced that they are planning to host a community week together with the people from the Google Cloud team. The main gist of this event was getting everyone to learn and use HuggingFace’s newly integrated JAX framework. But aside from just learning from tutorials, we were equipped with blazing fast TPUs thanks to the amazing Google Cloud team 🤯.
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Andrej Karpathy, Tesla’s AI Director released minGPT, a mini version to OpenAI’s GPT. Normally a GPT would have billions of parameters and would take hours to train. Karpathy’s approach is to provide a smaller version of GPT, hence the name minGPT.