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AI & Machine Learning News. 02, July 2018

Playing Card Detection

Generating a dataset of playing cards to train a neural net.

The notebook creating_playing_cards_dataset.ipynb is a guide through the creation of a dataset of playing cards. The cards are labeled with their name (ex: “2c” for “2 of spades”, “Kh” for King for hearts) and with the bounding boxes delimiting their printed corners.

This dataset can be used for the training of a neural net intended to detect/localize playing cards. It was used on the project Playing card detection with YOLO v3

https://github.com/geaxgx/playing-card-detection

2018-07-02 09:03:06+05:30 Read the Reddit Post.

CloudQuant Thoughts… Not really a news post, just a post on Reddit but excellent nonetheless. Thought I would take the opportunity to give it some exposure and pull the Reddit Post, Video and Github all together in one place.

 

Understanding and Building an Object Detection Model from Scratch in Python

When we’re shown an image, our brain instantly recognizes the objects contained in it. On the other hand, it takes a lot of time and training data for a machine to identify these objects. But with the recent advances in hardware and deep learning, this computer vision field has become a whole lot easier and more intuitive.

In this article, we will understand what object detection is and look at a few different approaches one can take to solve problems in this space. Then we will deep dive into building our own object detection system in Python. By the end of the article, you will have enough knowledge to take on different object detection challenges on your own!
2018-06-28 08:03:26+05:30 Read the full story.

CloudQuant Thoughts… This article goes into a little more detail on AI for computer vision if you are interested.

 

The Science Behind OpenAI Five – One of the Greatest Breakthroughs in the History of AI

Bill Gates called it a “huge milestone in advancing artificial intelligence” and the entire tech world was amazed last week when a bots developed by OpenAI were able to beat humans in a game of Dota 2 (Defence of the Ancients 2 – Valve Software). The accomplishment represents a major breakthrough in the history of artificial intelligence comparable with AlphaGo defeating Lee Sedol in 2016. OpenAI system leveraged state of the art deep learning research to that showed that AI is actually capable to deal with the messiness and complexity of physical environments in the real world.
2018-07-02 12:19:37.735000+00:00 Read the full story.

CloudQuant Thoughts… “Quantitively, the experience collected by the OpenAI Five system was estimated something around the 180 years per day. To accomplish that, the OpenAI team used approximately 128,000 CPU cores on the Google Cloud platform.” This is staggering, it’s not “comparable with AlphaGo”, the step-change it made is comparable to the step-change made by AlphaGo. ie we just took a huge step forwards.

 

AI is changing this industry. Now what?

I spent last week listening to experts in artificial intelligence talk about what AI can and will bring to the markets and the broader world.

What is patently clear is that AI is here now and is only going to expand. It is firmly in the hedge fund space today, as funds look for new ways to generate alpha.

A May report from Future Perfect Machine named Bridgewater Associates, Renaissance Technologies, DE Shaw, Two Sigma, Winton Capital Management, Schonfeld Strategic Advisors, PDT Partners, Man Group and Citadel as big AI users and cited a Barclays PLX survey that said “62 percent of hedge funds now use some type of AI process in collecting information, finding best execution, identifying market momentum and scanning information sources for signals and obscure patterns.”

Speaking at the WealthTech conference Morgan Slade, CEO at CloudQuant, said AI is being applied to only 50 or so of the 1500 alternative data sets available today. In the next few years, those alternative data sets may top 6,000, he said. Researchers who can mine that data fastest using AI may have the next edge.

2018-07-02 00:00:00 Read the full story.

CloudQuant Thoughts… A manual trader once described the US Equity market as being like a ring of salivating wolves waiting for a single chunk of meat to be dropped in the center of the circle, you have to be first to the meat. Well in the 10+ years I have been in this industry I have seen that race to the center increase in speed dramatically. Opportunities fade and new opportunities arise. You have to find new ways of discovering Alpha, of finding an edge. Alternative data-sets combined with AI are definitely the cutting edge today.

 

Relative Coin Rank as Investment Strategy

We’ve seen the rise and fall of many crypto coins over the last few years. Sometimes new projects seem to come out of nowhere, while other times a given coin rises steadily through the ranks until it gets noticed by the mainstream. With more than 1600 coins and tokens on the market today, things are getting awfully crowded, and it is difficult to learn enough know whether any given coin is worthy of inclusion in our portfolios.
2018-06-30 19:07:04.355000+00:00 Read the full story.

CloudQuant Thoughts... An interesting idea. Can you adapt it for US Equities? Give it a try at app.cloudquant.com. If you are successful we will back you with full force.

 

Morgan Stanley Hires University of Pennsylvania Artificial Intelligence Expert

Morgan Stanley, which aims to expand its use of artificial intelligence, has hired Michael Kearns to help guide the effort.

Kearns is a computer science professor at the University of Pennsylvania and has years of experience at Steve Cohen’s former hedge fund and other Wall Street firms. He will lead Morgan Stanley’s AI research and offer advice on deploying the technology for projects across the company, the New York-based firm said in a memo to employees Tuesday.

2018-06-28 11:02:07-04:00 Read the full story.

AI2 taps University of Washington researcher to lead $125M ‘common sense AI’ initiative

The Allen Institute for Artificial Intelligence in Seattle is making an ambitious bid to give AI common sense, a major factor in taking the technology beyond its current limitations. Now the institute has hired a new leader for the project: University of Washington Professor Yejin Choi. Choi will take the helm of Project Alexandria, the “common sense AI” initiative backed by $125 million from Paul Allen, the Microsoft co-founder and founder of the institute.

Her addition comes amid a fierce talent war over top AI researchers and engineers. Just in the past few months, Facebook poached a top AI2 researcher for its growing Seattle office. AI2, in turn, hired a key AI leader from Amazon’s Alexa division.
2018-06-29 16:45:36-07:00 Read the full story.

CloudQuant Thoughts… Both of these stories highlight the major issues in AI, that industry demand is outstripping educational supply, resulting in the private sector draining the educational sector of its best and brightest. This does not bode well for the future, we need the best in education to bring forth the best in the new generation of AI and ML engineers. Otherwise the supply will dry up.

 

TORA Recognized as Trading System of the Year at Global Investor Awards 2018

TORA announced that it was awarded Trading System of the Year at the Global Investor Awards in London. TORA added significant product enhancements to help investors adhere to MiFID II and other new global regulations, including:

A groundbreaking new AI-based tool within the TORA OEMS that analyses how effective trading decisions are at minimizing slippage versus a benchmark and that uses machine learning to provide pre-trade estimates to help traders improve execution quality. This real-time feedback mechanism was added as a direct response to the new, tougher, MIFID II best execution requirements.

An AI-based AlgoWheel. This allows traders to see a slippage ranking of normalized broker algos and if required, automates the execution of low-touch orders to free more time to focus on high-touch orders.

2018-06-29 00:00:00 Read the full story.

CloudQuant Thoughts… AI and ML are affecting every aspect of trading from the data to the decision to the trade.

 

Lessons in artificial intelligence from Nest, Audi and a cockerel

Optimizing for the wrong Metric… The most important thing to get right about AI is what you are trying to optimize.

For all the hype that exists around the smart home, self-driving vehicles, fear about losing jobs, I have shut off the “auto-scheduling” on my Nest, despite being a machine learning expert. I have an Audi that I find impossible to drive smoothly. And I grew up on a farm with a Cockerel that was relentless in his attacks on my legs. What do all these things have in common? Systems optimizing the wrong metric!

2018-06-30 22:30:57-07:00 Read the full story.

CloudQuant Thoughts… A very entertaining and thought-provoking article. As someone who developed a number of successful auto-trading systems prior to the AI revolution, the best idea was rarely the one you set out to research.

 

An Extra CloudQuant Thought… We also note the number of stories “below the fold” where Microsoft is being mentioned. This is obviously a direct result of their change of direction under Satya Nadella.

 


Below the Fold

 

Improving Marketing Attribution With Machine Learning (Interview with Max Sklar of Foursquare)

As part of our AI For Growth executive education series, we interview top executives at leading global companies who have successfully applied AI to grow their enterprises. Today, we sit down with Max Sklar, Head of Machine Learning Attribution at Foursquare.

User attribution, especially between offline and online worlds, is a persistent challenge for marketers. Using novel machine learning techniques on top of Foursquare’s incredible data trove of consumers’ physical behavior, Max enables enterprise customers to identify when online campaigns have driven offline revenue and convert real-world foot traffic into long-term digital customers and social media fans.

2018-06-25 19:26:04+00:00 Read the full story.

 

The Top GitHub Repositories & Reddit Threads Every Data Scientist should know (June 2018)

 

  • Facebook’s DensePose – identifies more than 5000 nodes in the human body (for context, other approaches operate with 10 or 20 joints).
  • NLP Progress – This repository has been created especially to track the progress in the NLP field
  • MLflow – Databricks open source a solution to all ML framework challenges. that manages the entire machine learning lifecycle
  • Salesforce’s decaNLP – sentiment analysis model that can also do semantic parsing and question answering at the same time
  • Reinforcement Learning Notebooks – Reinforcement learning is becoming popular by the day and so is the open source community for it
  • Playing Card Detection with YOLOv3 – thread has a lot of useful information on how the technique was created
  • OpenAI Five – a group of 5 neural networks designed and developed to beat human opponents in the popular Dota 2 game
  • What ML Hypothesis are you Curious About but are Hoping Someone Else will Research it? – This discussion is like a wish list of what data scientists and machine learning practitioners want to see
  • Setup that Data Scientists use for Machine Learning – Read this thread to find out what other data scientists use for building their ML
  • Practical Use Cases for Reinforcement Learning – people already working in this field give their take on where they see RL penetrating in the near future

2018-07-02 09:03:06+05:30 Read the full story.

 

Cloudera partners with MetiStream to launch Ember, an analytics platform for health care providers

Machine learning and analytics provider Cloudera is teaming up with health care analytics company MetiStream to launch a new health-focused, machine learning-powered medical records solution for hospital systems and outpatient clinics. Today, the firms jointly announced Ember, a product that “accelerates the time to patient insight” from handwritten clinical notes and other medical data.

2018-07-02 00:00:00 Read the full story.

 

Top 5 Mistakes of Greenhorn Data Scientists – Towards Data Science

You prepared well to finally become a Data Scientist. You participated in Kaggle competitions and you binge watched Coursera lectures. You feel prepared, but the work as a real-life Data Scientist will prove vastly different from what you might expect.

2018-06-30 17:15:15.404000+00:00 Read the full story.

 

Here Are Free AI Learning Resources For Beginners

Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning. In fact, Google’s Python-based Deep Learning framework Tensorflow has helped many a developer get up to speed with the technical concepts. Besides videos and free online courses, you must also have a reading list that helps you cover the math and statistics behind the algorithms.
2018-07-01 06:24:01+00:00 Read the full story.

 

The AI bubble won’t burst anytime soon, but change is on the horizon

During an AI podcast a couple months ago, someone asked me if AI would be the next big tech bubble to burst. This question led me to think about what AI is today and where it’s headed. What is AI, really? It’s a next-generation network and database tool that’s short for “artificial intelligence.” AI just sounds sexier. In fact, AI today is not really what people think it is. AI theories and algorithms have been around for decades.

2018-07-01 00:00:00 Read the full story.

 

Google Home’s language expansion leaves Alexa behind

With the recent launch of Spanish language integration for Google Home, Google is poised to gain even more ground against Amazon’s Alexa-powered devices. The addition of Spanish language functionality expands Google’s share in the U.S. digital assistant market and into Mexico and Spain. Alexa currently supports only three languages, including English, German, and Japanese, while Google Home is set to offer over 30 languages before 2019. As Alexa falls behind, many wonder why it has taken so long to make digital assistants multilingual.
2018-06-30 00:00:00 Read the full story.

 

Microsoft improves facial recognition to perform well across all skin tones

That improvement addresses recent concerns that commercially available facial recognition technologies more accurately recognized gender of people with lighter skin tones than darker skin tones, and that they performed best on males with lighter skin and worst on females with darker skin.

With the new improvements, Microsoft said it was able to reduce the error rates for men and women with darker skin by up to 20 times. For all women, the company said the error rates were reduced by nine times. Overall, the company said that, with these improvements, they were able to significantly reduce accuracy differences across the demographics.

2018-06-26 02:14:03-07:00 Read the full story.

 

IBM Addresses AI Bias with Massive Image Archive

Bias isn’t only about a person’s predetermined views affecting his or her opinion on a particular topic. It’s also an important factor in how accurate information from a query using artificial intelligence turns out.

Because of that belief, and because an AI system is only as good as the data upon which it is trained, IBM revealed June 27 that it will soon make available to the global research community:

  • A dataset of 1 million images to improve facial analysis system training. This archive will be five times larger than the largest face image dataset available today, and it is specifically designed to reduce sample selection bias.
  • A dataset of 36,000 facial images–equally distributed across various attributes– that algorithm designers can use to evaluate bias in their own facial analysis systems. This will specifically help algorithm designers to identify and address bias in their facial analysis systems. The first step in addressing bias is to know there is a bias, and that is what this dataset will enable.

The facial attribute and identity training dataset is annotated with attributes and identity, using geo-tags from Flickr images to balance data from multiple countries and active learning tools to reduce sample selection bias, the company said.

2018-06-27 00:00:00 Read the full story.

 

Ridge Regression Vs Lasso: 2 Popular ML Regularisation Techniques

In the case of ML, both ridge regression and Lasso find their respective advantages. Ridge regression does not completely eliminate (bring to zero) the coefficients in the model whereas lasso does this along with automatic variable selection for the model. This is where it gains the upper hand. While this is preferable, it should be noted that the assumptions considered in linear regression might differ sometimes.

Both these techniques tackle overfitting, which is generally present in a realistic statistical model. It all depends on the computing power and data available to perform these techniques on a statistical software. Ridge regression is faster compared to lasso but then again lasso has the advantage of completely reducing unnecessary parameters in the model.

2018-06-28 05:44:43+00:00 Read the full story.

 

RippleMatch uses AI to help students line up work after college

For most young adults, finding work out of school isn’t exactly a walk in the park. Two-thirds of recent college graduates struggle to launch their careers in the first few years, according to There Is Life After College author Jeffrey Selingo, and as many as 49 percent of them don’t land a job related to their field of study.

That’s why Yale graduates Eric Ho and Andrew Myers created RippleMatch, a machine learning-powered recruitment tool for college students. It recently raised $3 million in a funding round led by Accomplice and Bullpen Capital.

2018-06-29 00:00:00 Read the full story.

 

Niantic Opens AR Platform To Third-Party Developers, Shows Off Experimental Capabilities

Niantic Labs, the developer behind “Pokémon Go,” has announced that it is planning to open its augmented reality platform to third-party developers. The company also shared its vision for the future of its AR platform, which includes advancements in machine learning and computer vision.

Niantic has acquired the computer vision and machine learning company Matrix Mill and established a new office in London. Niantic plans to coordinate with Matrix Mill and Escher Reality to continue the advancement and development of its Real World Platform

2018-06-29 05:46:14-04:00 Read the full story.

 

How The Trade Desk Uses AI to Find ‘Perfect’ Impressions for Advertisers

Here’s a thought: Why not run digital advertising campaigns in a similar fashion to the stock market, using programmatic methods and new-gen IT? How about letting a few knowledgeable humans together with machine learning and analytics engines figure out where the perfect impressions are–on any device–that present buying opportunities relevant to us today?

That’s exactly what global ad tech provider The Trade Desk does on a 24/7 basis.

2018-06-27 00:00:00 Read the full story.

 

IT Science Case Study: Detecting Advanced Cyber Threats

IBC’s decision to use BluVector Cortex resulted in significant success. In 2017, IBC faced a new zero-day ransomware threat known as Jaff. Before news about the new zero-day malware broke publicly, IBC’s threat team observed more than 2,000 instances of Jaff in just a week. Thanks to the BluVector Cortex platform, using its Machine Learning Engine (MLE) to sort through the millions of files on the network, the threat team detected Jaff before it even had a name. With that knowledge, the team then used its containment software to halt the further spread of the malware.

2018-06-29 00:00:00 Read the full story.

 

30 Free Resources for Machine Learning, Deep Learning, NLP & AI

Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.

This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year. They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that).

2018-06-30 00:00:00 Read the full story.

 

How Artificial Intelligence Is Disrupting the ETF Industry

The robots are here. Artificial intelligence (AI) continues to build its presence across global industries, so its progression into the asset management sector is only natural.

AI is already disrupting the ETF industry and it is having spillover effects into other investment vehicles. Millions of market signals, news articles, and social media posts are processed by currently operating funds to produce thousands of hypothetical test portfolios which are further distilled down into daily trade recommendations. There has been an increase in the number of usually tight-lipped hedge fund managers admitting to using AI after these AI ETFs launched. Others will eventually catch on that more cost-efficient asset management structures exist through the use of AI.

For all its advances, it’s unlikely that AI will immediately replace human managers and analysts. But what it will do is to allow them to more efficiently manage the overwhelming amounts of market data.

2018-07-02 08:30:04-04:00 Read the full story.

 

Microsoft Goes Silo-Busting for Enterprise Cloud Analytics

Gathering insights from information placed on Microsoft’s cloud-based storage and big data analytics platforms is about to get easier for the company’s enterprise customers.

On June 27, Microsoft unveiled new cloud capabilities that further lower the barriers to big data analytics. They include Azure Data Lake Storage Gen2, a service that brings “together the notion of a Hadoop compatible file system with a scale-out object cloud storage platform,” namely Azure Blob Storage.
2018-06-27 00:00:00 Read the full story.

 

Microsoft’s Move To Launch ‘Research Open Data’ Is A Revolutionary Way To Compete With Google And AWS

Dubbed as an excellent open data effort by one of the leading cloud providers, Microsoft is striving hard to gain developer and community trust by embracing the open data movement with “Research Open Data”.

They plan for it to be an excellent collection of free datasets to push state-of-the-art research in areas such as natural language processing, computer vision, and domain-specific sciences. The datasets are available in several categories like Biology, Computer science, Engineering, Information science, Mathematics, Physics, Social Sciences.

2018-06-30 12:28:53+00:00 Read the full story.

 

How Analytics-Driven Store Clustering Can Drive Sales And Profits In Retail

Personalisation is the new mantra for retailers today. However, that should not be limited only for the customers but also be adopted as a strategy towards growing store sales. ‘One-size-fits-all’ approach would no longer work for achieving strong store-level growth and it is important to customise a strategy for each store.
2018-06-29 04:56:10+00:00 Read the full story.

 

Microsoft’s Enterprise IoT Push Moves to the Intelligent Edge

Microsoft’s internet of things (IoT) product strategy for enterprises just took a major turn.

Azure IoT Edge is generally available, the Redmond, Wash., software maker announced on June 27, bringing with it the quality and support assurances required for production IoT deployments that enable the intelligent edge at scale. The cloud-based service provides tools that enterprises can use to deploy, secure and run artificial intelligence (AI) and data analytics workloads on edge IoT devices and systems.

2018-06-28 00:00:00 Read the full story.

 

Noodle.ai Raises $35 Million In Series B Funding From Dell And TGP Growth

Noodle.ai, a noted provider of enterprise artificial intelligence applications, announced in an official statement that they have raised $35 million in a Series B funding round, bringing their fundraising total to $51 million. The company will use the new funds to expand their suite of applications, which help key industries predict the future and make better business decisions.

2018-06-27 10:35:03+00:00 Read the full story.

 

Investors to use less sell-side research, more AI

Institutional investors plan to use less sell-side research in the coming years, relying more on proprietary in-house work that takes advantage of AI, according to a study commissioned by Thomson Reuters.

2018-06-29 00:01:00 Read the full story.

 

More States Opting To ‘Robo-Grade’ Student Essays By Computer

Here’s a little pop quiz. Multiple-choice tests are useful because:

  • A: They’re cheap to score.
  • B: They can be scored quickly.
  • C: They score without human bias.
  • D: All of the above.

It would take a computer about a nano-second to mark “D” as the correct answer. That’s easy. But now, machines are also grading students’ essays. Computers are scoring long-form answers on anything from the fall of the Roman Empire, to the pros and cons of government regulations.

2018-06-30 00:00:00 Read the full story.

 

Artificial Nociceptors For AI, A Novel Approach To Comprehend Danger

The developments in today’s artificial intelligence applications are humongous and have reached a top-notch status in research. Robots, for example once had limited applications in niche areas, but are now being used in various business and commercial areas. AI systems such as robots require near-perfect sensory components to collect and analyze data for accurate functioning.

Recent trends in AI have also focused on innovations in hardware, apart from the methodology. Be it in the form of insanely smaller integrated chips or powerful and fast processors, the pace in bringing all these elements on a practical viewpoint for AI has been magnificent. One unique biological component which has slowly been enticing AI researchers towards hardware implementation is nociceptor.
2018-06-28 11:16:24+00:00 Read the full story.

 

Deep Learning Tensorflow Benchmark: GeForce Nvidia 1060 6GB Vs Intel i5 4210U

The use of GPUs in the 3D gaming realm has given rise to a high-definition gaming experience for gamers all over the world. Now, these mighty devices are being used in the world of deep learning to produce robust results — exactly 100 times faster than a CPU.

The reason why GPU is so powerful is because the number of cores inside it are three to five times more than the number of cores in a CPU, all of whom work parallelly while computing. In this article, we shall be comparing two components of the hardware world — a CPU, an Intel i5 4210U vs a GPU, a GeForce Nvidia 1060 6GB. With the help of one basic high-dimensional matrix multiplication, the famous MNIST dataset, we shall compare the computation power and speed of these devices.

2018-06-26 12:13:20+00:00 Read the full story.

 

Doubting Nvidia’s (NASDAQ:NVDA) Future Is Dumb

Doubting Nvidia (NVDA) is a dumb move. Shares of California-based Nvidia have slumped slightly in recent trading, down about 7% for the week. The stock has traded in the red in 11 of the last 16 sessions.

About 60% of Nvidia’s revenue is owed to PC gaming and cryptocurrency mining graphics processing units (GPUs), Davuluri said. But don’t think Nvidia is any one-trick pony when it comes to the chips. The mix of product and profit will increase, Davuluri said. “[Gaming and AI] will come closer to intersecting as a portion of sales,” the analyst predicted. “There’s definitely a mix shift within the organic growth itself.”

As for datacenter revenue, try this on for size: sales rocketed 132.8% year-over-year in 2017 for the unit.

2018-07-02 06:45:00-04:00 Read the full story.

 

How AI could improve the quality of end-of-life care

The means to predict mortality using artificial intelligence could be a transformative factor in the future of palliative health care. While this topic may seem a bit morbid, AI has the potential to help medical care providers and doctors significantly improve the delivery of patient care in hospice situations.

2018-06-29 00:00:00 Read the full story.

 

Another Robotics ETF is Here

There is momentum for investment vehicles with exposure to the artificial intelligence (AI) and robotics theme. Issuers of exchange-traded funds (ETFs) are racing to meet that demand, as highlighted by the debut of another AI and robotics ETF. On Thursday, the iShares Robotics and Artificial Intelligence ETF (IRBO) debuted.
2018-06-29 07:10:00-06:00 Read the full story.

 

How science fiction can predict the future and help tech innovators make better decisions

Organizations may do better in planning for the future by thinking less like business leaders and more like science-fiction writers.

That’s the idea behind the Seattle startup Scout, a subscription website, and community that develops near-term, what-if scenarios based on deep analysis of current technologies and trends. If using science fiction and analysis in this way sounds untried, Scout co-founder Berit Anderson said it reflects the approach of well-known tech industry figures such as Elon Musk and Vint Cerf.

“I really saw how strategic foresight and science fiction impacted and shaped their worldview,” Anderson said. “Not just how they think about the future, but also (how) a lot of science fiction creates kind of a blueprint for some of the world’s top technologists and CEOs.”

2018-06-25 13:13:21-07:00 Read the full story.

 

Weekly Selection — Jun 29, 2018 – Towards Data Science

 

  • How Bayesian statistics convinced me to hit the gym
  • Getting started with reading Deep Learning Research papers: The Why and the How
  • An Introductory Example of Bayesian Optimization in Python with Hyperopt
  • Deep Learning on the Edge
  • The 10 coolest papers from CVPR 2018
  • The Data Science Bubble
  • Data Science for Startups: Deep Learning
  • Towards Rapid Discovery of Viable Pipelines
  • Analyse a Soccer game using Tensorflow Object Detection and OpenCV
  • Winning the War Against Imbalanced Data

2018-06-29 15:30:49.135000+00:00 Read the full story.

 


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