Deep Learning on your phone: PyTorch C++ API for use on Mobile Platforms
The PyTorch Deep Learning framework has a C++ API for use on mobile platforms. This article shows an end-to-end demo of how to write a simple C++ application with Deep Learning capabilities. The same code can be built on Android and iOS platforms using the same API. For a general introduction on how to deploy a model to production, please see this article.
Build Your Own Automated Machine Learning App
In this article, we will create an automated machine learning web app you can actually use. We will implement a version of the TPOT example we looked at in my previous article as a Streamlit app. We are creating a script to automate the optimization of preprocessing and modeling of a classification task on the iris and digits datasets. With a few modifications, you should also be able to get the sandbox running with other datasets.
Data Scientists: How to Sell Your Project and Yourself
In the data science industry, it is not enough to be a skilled data scientist as you need to sell your project and yourself. The first step of selling an idea or yourself is developing a solid elevator pitch that provides your value proposition and how your project is different from others. In this blog post, we will discuss how elevator pitches work and what goes into them.
The dos and don’ts of machine learning research
Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don’ts for machine learning research. The paper, published on the arXiv preprint server, is aimed at academic researchers but is also useful for developers creating machine learning models for real-world applications. Lones reiterates the importance of paying extra attention to data across the machine learning lifecycle.
Sequence Modeling Solutions for Reinforcement Learning Problems
In this post, we explore whether we can tackle the reinforcement learning problem with the toolbox of sequence modeling. The end result is a generative model of trajectories that looks like a large language model and a planning algorithm. We treat these trajectories as unstructured sequences of discretized states, actions, and rewards, and train the Transformer architecture using the standard cross-entropy loss.
The Ultimate Guide To Different Word Embedding Techniques In NLP
Recent advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG) have skyrocketed the ability of computers to better understand text-based content. NLP-powered systems must be able to recognize words, grammar, and a whole lot of language nuances. For computers, this is easier said than done because they can only comprehend numbers. To bridge the gap, NLP experts developed a technique called word embeddings that convert words into their numerical representations.
Easy Synthetic Data in Python with Faker
Faker is a Python library that generates fake data to supplement or take the place of real world data. See how it can be used for data science. Faker can be installed with pip and instantiated an instance of Faker to use. The Faker constructor takes a performance-related argument called. It specifies whether to attempt to have the frequency of values match real-world frequencies.
The Common Misconceptions About Machine Learning
There is a hype train going on about ML (Machine Learning) and many beginners are getting to be the victims of this hype train as they are getting in for the wrong reasons. This article clearly describes the ground truth realities about learning new ML skills and eventually working professionally as a machine learning engineer. We will be learning a lot about the real-life problem faced by a beginner getting into the machine learning field.
How Sparsity Enables Energy Efficient Deep Learning Networks
DeepMind’s AlphaGo, a computer Go program developed by Google DeepMind, might have beaten 18-time world champion Lee Sedol at a Go match, but Lee used a mere 20 Watts to operate, less power than a lightbulb. In contrast, AlphaGo used 1,920 CPUs and 280 GPUs, which is 50,000 times as much power as the brain uses. Training a deep learning model can produce 626,000 pounds of carbon dioxide, equivalent to the lifetime emissions of five cars.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
In real-world applications, distribution shifts are ubiquitous. In medical applications, we might train a diagnosis model on patients from a few hospitals, and then deploy it more broadly to hospitals outside the training set. To be able to reliably deploy ML models in the wild, we urgently need to develop methods for training models that are robust to distribution shifts. ICML 2021 paper presents WILDS, a curated benchmark of 10 datasets that reflect natural distribution shifts arising from different cameras, hospitals, experiments, demographics, countries, time periods, users, and codebases.
Fast AND Accurate Sparse Networks
Deep neural networks (DNNs) have become the models of choice for an ever-increasing range of application spaces. DNNs have delivered State-of-the-Art results in every space into which they are deployed. These DNN models are often incredibly complex, and it takes significant computational resources to both train and deploy these models. The resulting costs are real; both in terms of cloud costs and the cost to the planet from the associated energy consumption.
25 Github Repositories Every Python Developer Should Know
Github is like a Goldmine for developers where gold is the code written by other developers. With the help of GitHub, you can learn how to write better code, how good code looks. Most of the repositories included in this article are based on data science and machine learning. Let’s divide the list of repositories into five parts. Python By The Algorithm is a collection of Jupyter notebooks to learn python.
A First-Principles Theory of Neural Network Generalization
Many empirical phenomena, well-known to deep learning practitioners, remain mysteries to theoreticians. Perhaps the greatest of these mysteries has been the question of generalization: why do the functions learned by neural networks generalize so well to unseen data? In our recent paper, we derive a first-principles theory that allows one to make accurate predictions of neural network generalization (at least in certain settings)
Accelerating AI with MLOps
Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential. There are still very few established standards for developing AI/machine learning (ML) models. Models are often created from scratch without consistent software development processes, testing procedures or KPIs for measuring their performance.
NLP for Business in the Time of BERTera: Seven Misplaced Passions
For the past two years, Inscripta AI has been working with organizations looking to integrate NLP/AI into their workflows and solutions. This article is a brief summary of our observations on some common client misperceptions with respect to recent developments in NLP, especially the use of large-scale models and datasets. These points are relevant primarily to business contexts where AI integration is in its early stages.
Artificial intelligence vs neurophysiology: Why the difference matters
This article is a continuation of the article titled Symbiosis Instead of Evolution, A New Idea about the Nature of Human Intelligence. It is important to remember these words for everyone who wants to create artificial intelligence. The human brain with a high degree of probability may turn out to be a binary system, consisting of two functional schemes of response to excitation: reflex and intellectual.
What Comes After HDF5? Seeking a Data Storage Format for Deep Learning
This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists. NASA’s Earth Observing System satellites gather around 16 TBs of data a day in the HDF5 format. The layout of HDF files makes them difficult to query efficiently on cloud storage systems (like Amazon’s S33), where ML datasets increasingly are stored.
eBook: 101 Ways to Use Third-Party Data to Make Smarter Decisions
AWS Data Exchange has created a new eBook, 101 Ways to Use Third-Party Data to Make Smarter Decisions. This innovative resource is designed to inform readers on how to use third-party data to scale their business. Practical ways to create a data-driven culture with front-line examples. How to thrive in the new data economy by leveraging data-centric products in the cloud.
What to expect from OpenAI’s Codex API
OpenAI will make Codex, its AI programmer technology, available through an application programming interface. Codex is a descendant of GPT-3, a very large language model OpenAI released in 2020 and made available through a commercial private beta API. The current version of Codex has a 37-percent accuracy on coding tasks as opposed to the zero percent model. The machine learning model is already used in Copilot, another beta-test code generation product hosted by GitHub.