With Bitcoin surpassing $50,000 for the first time in yesterday’s bull run and the backdrop of the GME stock where Reddit traders tried their hand at besting large hedge funds such as Melvin Capital. There is a large influx of young investors that are willing to try their hand at riskier investment products who would naturally gravitate to cryptocurrency due to the sudden surge in popularity in Dogecoin.
I firmly believe in doing your own research and not solely depending on one source as truth.
This guide is my own research on the available products as a Singaporean.
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A significant problem in the arms race to produce more accurate models is complexity, which leads to the problem of size. These models are usually huge and resource-intensive, which leads to greater space and time consumption. (Takes up more space in memory and slower in prediction as compared to smaller models)
A large model size is a common byproduct when attempting to push the limits of model accuracy in predicting unseen data in deep learning applications. For example, with more nodes, we can detect subtler features in the dataset. However, for project requirements such as using AI in embedded systems…
With the recent release in TensorFlow lite for the Raspberry pi, previously slow model prediction rates on embedded systems has been vastly improved. Small single-board computers such as the Raspberry Pi has consequentially become more viable as devices to be used for machine learning. This is true especially when coupled with model compression techniques such as post-quantisation and model pruning.
I have encountered many countless pitfalls when I was attempting to install TF 2 onto my Raspberry and I foresee many might too, as such I came up with this article to provide a clear work-able step guide.
Currently, there is a disparity. There are many Binance products with little to no reviews on the experiences of these products. Since many investors do not have the time to research and try every single product such as Launchpad, Liquid Swap or Binance Earn, the cost is missing out on many freebies that you could earn while you sleep.
So went ahead to try many of these products and realised that
1. Finding useful information was a nightmare.
2. I now regret not trying these products earlier
As such, to prevent others from feeling the same way, I will be…
Learn about advanced yet simple concepts of Streamlit that anyone can learn to create your own Stock Tracker Dashboard backed.
Streamlit has been burning brightly as the frontliners of data science visualisation. Transforming data insights to interactive and informative dashboards, Streamlit has and will be an essential tool for all data scientists attempting to bridge the gap between them and other data scientists, business users, and managers.
This originated from my frustration in trying to monitor my invested stocks. …
Near-Field-Communication (NFC) isn’t new technology. With the events of Covid 19, there has been a surge in contactless NFC usage as seen by the new trend of mobile payment like Google Pay and Apple Pay. This is similar to its well-known brother, QR codes.
It has come a long way. First used in 1997 for Hasbro Star Wars toys, the now highly accessible tech which currently exists in around 73% of smartphones, is an untapped source for innovation.
Similar to my previous article on installing TF lite for Raspberry Pi, finding useful NFC material was a chore. …