If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Looking to meet enterprise needs in the machine learning space, Oracle is making its Tribuo Java machine learning library available free under an open source license. With Tribuo, Oracle aims to make it easier to build and deploy machine learning models in Java, similar to what already has happened with Python. Released under an Apache 2.0 license and developed by Oracle Labs, Tribuo is accessible from GitHub and Maven Central. Tribuo provides standard machine learning functionality including algorithms for classification, clustering, anomaly detection, and regression. Tribuo also includes pipelines for loading and transforming data and provides a suite of evaluations for supported prediction tasks. Because Tribuo collects statistics on inputs, Tribuo can describe the range of each input, for example.
TPUs (Tensor Processing Units) are application-specific integrated circuits (ASICs) that are optimized specifically for processing matrices. Google Colab provides experimental support for TPUs for free! In this article, we'll be discussing how to train a model using TPU on Colab. Specifically, we'll be training BERT for text classification using the transformers package by huggingface on a TPU. Since the TPU is optimized for some specific operations, we need to check if our model actually uses them; i.e. we need to check if the TPU actually helps our model to train faster.
With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.
Artificial intelligence (AI) is a hype phrase that comes with a lot of baggage: will its potential ever be realized; will it enhance humans, or make them obsolete; is it really that revolutionary? But one area of the debate that is often overlooked -- and is one of the more positive aspects of modern innovation, in fact -- is the way big tech companies like Google, Amazon, Facebook, and Microsoft are working together to help progress AI. These companies have been the focus of much criticism over the last few years, consolidating their influence and dominating specific parts of our lives but when it comes to AI, something is different. That something is open source. The sheer number of open source tools available to developers -- from libraries to frameworks, IDEs, data lakes, streaming, model serving, and inference solutions, and even the recent end-to-end tool aggregator, Kubeflow -- means businesses can now harness all the knowledge they have accumulated over the years.
Now, let's jump to the implementation. Firstly, we need to, obviously, import some libraries. The first thing we do inside .fit() is to concatenate an extra column of 1's to our input matrix X. This is to simplify our math and treat the bias as the weight of an extra variable that's always 1. The .fit() method will be able to learn the parameters by using either closed-form formula or stochastic gradient descent.
Earlier, the field of AI was more academic and was mostly researched by PhD holders. However, the area has now become approachable with software developers and other tech professionals taking advantage of the new AI paradigms to build a whole new scenario of use cases for the world. More developers are becoming interested in exploring tools and techniques such as TensorFlow and other open-source tools to explore the field. With frameworks such as TensorFlow, companies such as Google are also making it easy for a software developer who doesn't necessarily have academic expertise or a PhD in AI/ML, allowing them to ML models. Developers are using machine learning in their applications as a way to stand out in the crowd.
We are a group of talented and passionate engineers and data scientists working together to discover and provide valuable insights for our customers. We leverage state-of-the-art machine learning techniques to provide our users with these unique insights, best practices, and solutions to the challenges they face in their workplace. Problems and solutions typically center around aspects of the Vision platform such as image recognition, natural language processing, and content recommendation. As a Data Scientist, you will build machine learning products to help automate workflows and provide valuable assistance to our customers. You'll have access to the right tools for the job, large amounts of quality data, and support from leadership that understands the full data science lifecycle.
Keras is a deep learning neural network library written in Python that works on a high level. It is running on top of backend libraries like Tensorflow (or Theano, CNTK, etc.) which is capable of doing calculations on a low level, like multiplying tensors, convolutions and other operations. This library has many pros, like, it is very easy to use once you get familiar with, it allows you to build a model of neural network in a few lines of code. It is highly supported by the community, it can run on top of many backend libraries as we mentioned earlier, can be executed on more than one GPUs and so on. In this example, we are going to install Tensorflow, as it is the most used and the most popular one.
Recently, developers from Google's Magenta introduced a virtual room in the browser known as Lo-Fi player that lets you play with various musical beats of instruments. Lo-Fi is basically a music generating tool which allows you to select and create music of your choice. In a blog post, the developers of this AI system stated that if anyone has ever listened to the popular Lo-Fi Hip Hop streams while working and at the same time imagined if they were the producer, it will now allow them to create their own music and vibe. The developers chose the Lo-Fi Hip Hop because it's a popular genre where the structure of the music is relatively simple. According to them, this limited flexibility assisted in ensuring that the music always makes some sense.
Hundreds of thousands of machine learning experiments are conducted globally every single day. Machine learning engineers and students conducting those experiments use a variety of frameworks like TensorFlow, Keras, PyTorch, and others. These models form the foundation of every AI-powered product. So where and how does the ONNX library fit into Machine Learning? What is it exactly, and why did big names like Microsoft and Facebook introduce this library?