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) …
Here's how we used the hundreds of thousands of publicly accessible repos on GitHub to learn more about the current state of data science. Inspired by research carried out 2 years ago by the Design Lab team at UC San Diego, the JetBrains Datalore team decided to download all Jupyter notebooks accessible in October 2019 and October 2020 to gather statistics on the tools that the global DS community has been using in recent years. By October 2020 this number had grown 8 times, and we were able to download 9,720,000 notebooks. We made this dataset publicly available, and you can find the instructions for accessing it at the bottom of the post. Feel free to play with it and share your insights with us by mentioning @JBDatalore on Twitter, or write to us at firstname.lastname@example.org.
Why everyone wants to learn TensorFlow in deep learning, as we deep dive into the machine learning projects, we use the "sklearn" library, and when we talk about deep neural networks, TensorFlow comes into the picture. Anaconda distribution is perfect for data science and machine learning with the pre-installed library packages, but we have to install TensorFlow explicitly because it does not come in anaconda distribution. To download the TensorFlow write the following command in the anaconda prompt and press enter. TensorFlow is a library for deep computational processes in many applications and is widely used in deep neural networks. Before knowing what tensor is, go through with some definitions.
Deep learning framework with an interface or a library/tool helps Data Scientists and ML Developers to bring the deep learning models into life. Deep Learning a sub-branch of machine learning, that puts efficiency and accuracy on the table, when it is trained with a vast amounts of bigdata. TensorFlow developed by the Google Brain team, is inarguably one of the most popular deep learning frameworks. It supports Python, C, and R to create deep learning models along with wrapper libraries. It is available on both desktop and mobile. The most popular use case of TensorFlow is the Google Translate integrated with capabilities like NLP, text classification, summarization, speech/image/handwriting recognition and forecasting.
In this tutorial you will learn how to implement and train siamese networks using Keras, TensorFlow, and Deep Learning. Practical, real-world use cases of siamese networks include face recognition, signature verification, prescription pill identification, and more! Furthermore, siamese networks can be trained with astoundingly little data, making more advanced applications such as one-shot learning and few-shot learning possible. To learn how to implement and train siamese networks with Keras and TenorFlow, just keep reading. In the first part of this tutorial, we will discuss siamese networks, how they work, and why you may want to use them in your own deep learning applications. From there, you'll learn how to configure your development environment such that you can follow along with this tutorial and learn how to train your own siamese networks.
You want a conversational artificial intelligence (AI) platform? No problem--you just need to choose one. But don't stop now: There are hundreds of options (from Kore.ai to SAP to Cisco's MindMeld to etc. etc.). Rasa's approach just might stand out. "We think that infrastructure for conversational interfaces in the long run will be open source," said Tyler Dunn, a product manager at Rasa.
The year 2020 saw many exciting developments in machine learning. As the year 2020 comes to an end, here is a roundup of these innovations in various machine learning domains such as reinforcement learning, Natural Language Processing, ML frameworks such as Pytorch and TensorFlow, and more. Arm-based Graviton processors went mainstream in 2020, which utilize 30 billion transistors with 64-bit Arm cores built by Israeli-based engineering company Annapurna Labs. AWS recently acquired it for powering memory-intensive workloads like real-time big data analytics. It showed a 40% performance improvement emerging as an alternative to x86-based processors for machine learning, shifting the trend from the Intel-dominated cloud market to Arm-based Graviton processors.
Machine learning and AI are new shining lights on information technology. Microsoft, as one of the biggest market sharer of IT finally announced ML.NET in May 2018 to aid .Net developers to achieve their goals. But it's quite new and compared to other technologies such as Python, Java, LISP, etc. there aren't yet enough sources in terms of open source projects, libraries, or 3rd tools. There is still a long way to go for .Net to catch up with other big techs to grow its community Fortunately, we don't have to wait until this happens, thanks to Microsoft Engineers they make ML.NET comes up with an adaptive development experience and supports Python models when used together with NimbusML. We all should appreciate Microsoft for amazing work!
Welcome to AI and Machine Learning for Coders, a book that I've been wanting to write for many years but that has only really become possible due to recent advances in machine learning (ML) and, in particular, TensorFlow. The goal of this book is to prepare you, as a coder, for many of the scenarios that you can address with machine learning, with the aim of equipping you to be an ML and AI developer without needing a PhD! I hope that you'll find it useful, and that it will empower you with the confidence to get started on this wonderful and rewarding journey. If you're interested in AI and ML, and you want to get up and running quickly with building models that learn from data, this book is for you. If you're interested in getting started with common AI and ML concepts--computer vision, natural language processing, sequence modeling, and more--and want to see how neural networks can be trained to solve problems in these spaces, I think you'll enjoy this book.
You'll see our IPU-M2000 system significantly outperforms the Nvidia A100 DGX across the board, with orders of magnitude performance improvements for some models. Graphcore customers are already making big leaps forward with our second generation IPU systems – whether they prioritise faster time to result, model accuracy, better efficiency, lower TCO (Total Cost of Ownership) or the chance to make new breakthroughs in AI with the IPU. We've chosen a range of the most popular models our customers frequently turn to as proxies for their proprietary production AI workloads in natural language processing, computer vision and more, both in training and inference. We are also delighted to share results in this blog using our new PyTorch framework support. We are continuing to develop and expand this capability – you can find out more in our blog here.
Amazon SageMaker Neo was launched at AWS re:Invent 2018. It made notable performance improvement on models with statically known input and output data shapes, typically image classification models. These models are usually composed of a stack of blocks that contain compute-intensive operators, such as convolution and matrix multiplication. Neo applies a series of optimizations to boost the model's performance and reduce memory usage. The static feature significantly simplifies the compilation, and you can decide on runtime inference tasks such as memory sizes ahead of time using a dedicated analysis pass.