- Sundar Pichai at the company’s annual developer conference in Mountain View, experts are in short supply as companies in many industries rush to take advantage of recent strides in the power of artificial intelligence.
- At Google’s annual developer conference today, Pichai introduced a project called AutoML coming out of the company’s Google Brain artificial intelligence research group.
- The company is trying to lure new customers in the corporate cloud computing market, where it lags leader Amazon and second-place Microsoft (see “Google Reveals Powerful New AI Chip and is targeted at making it easier to use a technique called deep learning, which Google and others use to power speech and image recognition, translation, and robotics (see “10 Breakthrough Technologies 2013: Deep Learning”).
- “We do it by intuition,” says Quoc Le, a machine-learning researcher at Google working on the AutoML project.Last month, Le and fellow researcher Barret Zoph presented results from experiments in which they tasked a machine-learning system with figuring out the best architecture to use to have software learn to solve language and image-recognition tasks.On the image task, their system rivaled the best architectures designed by human experts.
- But like many ideas in the field of artificial intelligence, the power of deep learning is allowing new progress.
AI software that can help make AI software could accelerate progress on making computers smarter.
Continue reading “Why Google’s CEO Is Excited About Automating Artificial Intelligence”
- A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
- This book covering machine learning is written by Shai Shalev-Shwartz and Shai Ben-David.
- This introductory text on Bayesian machine learning is one of the most well-known on the topic as far as I am aware, and happens to have a free online version available.
- This is the soon-to-be-released-in-print deep learning book by Goodfellow, Bengio and Courville, which has a freely-available final draft copy on its official website.
- I wish you well on your quest to learn more about machine learning from free ebooks.
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
Continue reading “5 EBooks to Read Before Getting into A Machine Learning Career”
- I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
- Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
- Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
- More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
- It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.
Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.
Continue reading “An Overview of Python Deep Learning Frameworks”
- The gala awards event celebrating winners of NVIDIA’s Inception competition for AI startups had the trappings of Tinseltown, with one key difference: Its envelope was stuffed with $1.5 million in cash prizes, to be split among six of the world’s most promising AI startups.
- “Ten years ago, we started a journey of creating a new computing platform so that you guys could discover things and make a contribution to the world that otherwise would have been impossible,” said Jensen Huang, NVIDIA CEO and founder.
- “It’s great that a company like NVIDIA that’s defined the industry and is continuing to define it will help startups,” said Tanay Tandon, the 20-year-old founder of Athelas.
- “Winning this prize is the ultimate recognition from the deep learning industry because deep learning and NVIDIA are synonymous,” said David Eli, Deep Instinct CEO and co-founder.
- At the close of the event, winners stepped onto the stage for champagne toasts with Huang and other NVIDIA executives.
The gala event celebrating winners of NVIDIA’s Inception competition for AI startups had the trappings of Tinseltown. Judges chose top teams and runners-up in three categories.
Continue reading “These Six AI Startups Just Snagged a Share of $1.5 Million in Cash Prizes”
- Caffe2 is a deep learning framework enabling simple and flexible deep learning.
- Built on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind, allowing for a more flexible way to organize computation.
- Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning by leveraging community contributions of new models and algorithms.
- Caffe2 comes with native Python and C++ APIs that work interchangeably so you can prototype quickly now, and easily optimize later.
- Caffe2 is accelerated with the latest NVIDIA Pascal™ GPUs and scales across multiple GPUs within a single node.
Run deep learning training with Caffe2 up to 3x faster on the latest NVIDIA Pascal GPUs.
Continue reading “Caffe2 Learning Framework and GPU Acceleration”
- NVIDIA and Facebook today announced the result of our joint work to advance artificial intelligence with Caffe2, a new AI deep learning framework contributed by Facebook to the open-source community.
- NVIDIA and Facebook are delivering AI acceleration through our work on the Caffe2 deep learning framework.
- Thanks to our joint engineering, we’ve fine-tuned Caffe2 from the ground up to take full advantage of the NVIDIA GPU deep learning platform.
- It delivers near-linear scaling of deep learning training with 57x throughput acceleration on eight networked Facebook Big Basin AI servers with 64 NVIDIA Tesla P100 GPU accelerators.
- As part of the companies’ collaboration, the NVIDIA DGX-1 AI supercomputer will be the first AI system to offer Caffe2 within the optimized software stack for deep learning.
NVIDIA and Facebook today announced the result of our joint work to advance artificial intelligence with Caffe2, a new AI deep learning framework.
Continue reading “NVIDIA, Facebook Supercharge Caffe2 Deep Learning Framework”
- AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs.
- For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
- For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale.
- Launch instances of the AMI, pre-installed with open source deep learning frameworks (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
- Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of the managed AI services, the AI Platform offerings, and the AI Frameworks you can run on the AWS Cloud.
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
Continue reading “AI Tech Talk: An Overview of AI on the AWS Platform”