Why Is America So Obsessed With Ivy League Schools?
And what are these pollywogs After days, 12 countries, 13 cities, and a enough memories to fill more than a few travel journals, Spring Voyagers are now adventuring without their shipboard community alongside You are using an old version of Internet Explorer. Our site is developed with the latest technology, which is not supported by older browsers We recommend that you use Google Chrome for accessing our or any website. It is a FREE and modern web-browser which supports the latest web technologies offering you a cleaner and more secure browsing experience. Your browser does not support the video tag.
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- [New Research] How has Blogging Changed? 5 Years of Blogging Statistics, Data and Trends?
Do you have a clear idea who your ideal reader is and how you can help them solve their problems, answer their questions and achieve their aims? Is the traffic to your site relevant? Are you writing to engage your reader or to impress Google? How human is your writing? Time spent on each post is up. Length and exploration of depth is up.
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And the biggest mentioned challenge to not only attract attention but hold it. This is probably because search and social are dominated by digital monopolies, which control user experience. Google and Facebook have reduced clickthrough rates to websites in order to keep visitors on their platforms. Bloggers are reporting an easier time. There are a few big themes here.
Bonus: What are the biggest blogging challenges?
There seem to be two main factors that are the strongest indicators of success: depth and consistency. Bloggers who do more are getting better results. These are the bloggers most likely to report success:. Bloggers who are more consistent are getting better results. The respondents to this survey are self-described bloggers that we connected with over many years on social media and at live events.
The data set is heavily populated with my network, which skews toward LinkedIn users, B2B marketers and people in the US. Responses were gathered from July through September No one was incentivized to take the survey. This is a survey of bloggers individuals , not companies or brands groups. Here are the three ways we gathered responses along with our estimates for their contribution:.
This survey is well known in our industry. It shows that writing longer posts is good. Data was captured using a simple one-page survey of 19 questions.
Early versions of the blogging statistics and trends were sent to influencers to gather insights, which correlations and pivot tables were created at Orbit. The insights and data were analyzed in the wee hours in a dark kitchen. Finally, images and editing were handled by the small but powerful content team at Orbit. How is this survey promoted? Read our playbook here.
First, thank you respondents, all of you. You spent 3 minutes and 10 seconds on average. Thanks to the contributors.
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They are all my teachers. I recommend following, subscribing and reading each of them. We are grateful for your insights and for your help promoting the survey.
When I am updating older posts, especially those that still engage readers, I am making sure they appear properly on iPhones. With everyone looking down at that screen, modern readable fonts and formatting are breaking points worthy of discussion. Really helpful to get some data to assist me with crafting new content. Thank you. Share This.
By Andy Crestodina.
How long is the typical blog post? What content formats are bloggers using? What does the typical blog post include? How many draft headlines are bloggers writing?
The Law of Accelerating Returns | Kurzweil
How is your content typically promoted? How often do bloggers research keywords? Are bloggers using analytics? Are bloggers getting results from their content? Here are the final statistics, analysis and input from blogging and content marketing experts. How long does it take to write a blog post? The hidden state self. The np. Notice briefly how this works: There are two terms inside of the tanh: one is based on the previous hidden state and one is based on the current input.
In numpy np. The two intermediates interact with addition, and then get squashed by the tanh into the new state vector. Going deep. RNNs are neural networks and everything works monotonically better if done right if you put on your deep learning hat and start stacking models up like pancakes. For instance, we can form a 2-layer recurrent network as follows:. Getting fancy. The LSTM is a particular type of recurrent network that works slightly better in practice, owing to its more powerful update equation and some appealing backpropagation dynamics. Okay, so we have an idea about what RNNs are, why they are super exciting, and how they work.
This will then allow us to generate new text one character at a time. This training sequence is in fact a source of 4 separate training examples: 1. Concretely, we will encode each character into a vector using 1-of-k encoding i. We will then observe a sequence of 4-dimensional output vectors one dimension per character , which we interpret as the confidence the RNN currently assigns to each character coming next in the sequence. Since the RNN consists entirely of differentiable operations we can run the backpropagation algorithm this is just a recursive application of the chain rule from calculus to figure out in what direction we should adjust every one of its weights to increase the scores of the correct targets green bold numbers.
We can then perform a parameter update , which nudges every weight a tiny amount in this gradient direction. If we were to feed the same inputs to the RNN after the parameter update we would find that the scores of the correct characters e. We then repeat this process over and over many times until the network converges and its predictions are eventually consistent with the training data in that correct characters are always predicted next.
A more technical explanation is that we use the standard Softmax classifier also commonly referred to as the cross-entropy loss on every output vector simultaneously. The RNN therefore cannot rely on the input alone and must use its recurrent connection to keep track of the context to achieve this task. At test time , we feed a character into the RNN and get a distribution over what characters are likely to come next.
We sample from this distribution, and feed it right back in to get the next letter. Lets now train an RNN on different datasets and see what happens. Lets first try a small dataset of English as a sanity check. Without further ado, lets see a sample from the RNN:.