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Difference Between Deep Learning and Machine Learning in one Infographic

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Deep Learning is more powerful and flexible than Traditional Machine Learning. In fact Deep learning is also a Machine Learning type but have difference in many other ways. Traditional Machine learning has its own advantages. You have to choose and decide the best for your application.


What is the simplest entry into NN image classification systems, as a C-callable library?

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The data set would be astronomy sub-images that are either bad (edge of chip artifacts, bright star saturation and spikes, internal reflections, chip flaws) or good (populated with fuzzy-dot stars and galaxies and asteroids and stuff). Let's say the typical image is 512x512 but it varies a lot. Because the bad features tend to be big, I'd probably like to bin the images down to say 64x64 for compactness and speed. It has to run fast on tens of thousands of images. I'm sort of tempted by the solution of adopting PlaidML as my back end (if I understand what its role is), because it can compile the problem for many architectures, like CUDA, CPU-only, OpenCL.


5 Free Books To Take Your Data Science Skills to The Next Level

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As things stand, I am nowhere near where I aspire to reach as a Data Scientist. In my journey so far, I have met many helpful people and come across various useful resources. Whilst I pondered on…


Selecting the Right Bounding Box Using Non-Max Suppression (with implementation)

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There are various algorithms for object detection tasks and these algorithms have evolved in the last decade. To improve the performance further, and capture objects of different shapes and sizes, the algorithms predict multiple bounding boxes, of different sizes and aspect ratios. But of all the bounding boxes, how is the most appropriate and accurate bounding box selected? This is where NMS comes into the picture. The objects in the image can be of different sizes and shapes, and to capture each of these perfectly, the object detection algorithms create multiple bounding boxes.


China Looking to Lead on Robot Innovation - Stories - David South Consulting

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Since the 1950s, science fiction has been telling the world we will soon be living with robots. While robots have emerged, they have been mostly kept to heavy industry, where machines can perform dangerous, hot and unpleasant repetitive tasks to a high standard. But China is pioneering the move to mainstream robots in more public spheres. And the country is promising big changes in the coming decade. Robots, strange as it may seem, can play a key role in development and fighting poverty.


3 Things You Need to Know About Deep Learning

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Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.


You Won't Believe These 5 Companies Have AI Dept!

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Artificial Intelligence (AI) is on the verge of becoming one of the most competitive industry in the world as the big tech companies are racing on to become the leaders in this industry. Its ownership is from Ireland and scouts for talents related to the music industry. Audio-enabled search is being employed for searching what they require in A&R departments of record labels. Zach Miller-Frankel and Neil Dunne had found this company in Dublin in 2017. The technology can be used for searching specific criteria of their recruitments such as male musicians of a certain age within a certain area of given radius. It was founded in 1904 by François Coty; it is a multinational beauty company that is responsible for the manufacturing, marketing, distribution of fragrant products, cosmetics, skin care, nail care, hair care and so on.


Insight: How artificial intelligence helps firms stay relevant amid pandemic

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COVID-19 has put technology at the heart of many companies. Consumer behavior has shifted dramatically over the past few months, and ever more transactions are taking place online. While the pandemic has brought financial and operational challenges to all markets, technology, especially artificial intelligence (AI), proves that growth is still possible during times of crisis. Unfortunately, despite being around for quite some time, there are often misconceptions about what AI can and cannot do. Indonesia, with its large population and a deep smartphone penetration, presents a huge opportunity for data intensive technology.


Unpopular Opinion – Data Scientists Should Be More End-to-End - KDnuggets

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Recently, I came across a Reddit thread on the different roles in data science and machine learning: data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc. It's difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view. IMHO, I believe data scientists can be more effective by being end-to-end. Here, I'll discuss the benefits and counter-arguments, how to become end-to-end, and the experiences of Stitch Fix and Netflix. I find these definitions to be more prescriptive than I prefer. Instead, I have a simple (and pragmatic) definition: An end-to-end data scientist can identify and solve problems with data to deliver value.


The 6 Biggest Pitfalls That Companies Must Avoid When Implementing AI

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The age of AI is upon us and many companies begin to start their AI journey and reap the full potential of AI in their respective industries. But, some still consider AI as an immature technology with plenty of ways for it to go wrong. Therefore, before starting your long AI journey, there are some pitfalls you should avoid in implementing and developing AI solutions. They're a result of the anecdotal, personal and published experience of AI projects that could have gone better. Reinventing the wheel, that's the reasonable words to describe building an AI system that has become an industry standard.