Neural networks have many hyperparameters that affect the model's performance. One of the essential hyperparameters is the learning rate (LR), which determines how much the model weights change between training steps. In the simplest case, the LR value is a fixed value between 0 and 1. However, choosing the correct LR value can be challenging. On the one hand, a large learning rate can help the algorithm to converge quickly.

Despite vast differences in culture around the world, humanity's DNA is 99.9% similar. There are few attributes more central and universal to the human experience than our emotions. Of course, the broad spectrum of emotions we're capable of experiencing can be difficult to articulate. That's where this brilliant visualization by the Junto Institute comes in. This circular visualization is the latest in an ongoing attempt to neatly categorize the full range of emotions in a logical way.

Deep neural networks are becoming omnipresent in natural language applications (NLP). However, they require large amounts of labeled training data, which is often only available for English. This is a big challenge for many languages and domains where labeled data is limited. In recent years, a variety of methods have been proposed to tackle this situation. This article gives an overview of these approaches that help you train NLP models in resource-lean scenarios.

When we hear about artificial intelligence, we refer to what we see in films; it creates a perception that AI is driven by robots or things from another world. This thinking has been shaping nowadays, adapting to new applications, and growing in several sectors adhered to artificial intelligence. On average, 64 years ago, artificial intelligence originated from scientific studies without imagining what it would be capable of. Last year, I published an article that was a quick look at some of the most critical events in AI since its beginning and some interesting links. That article called "An easy guide to the history of Artificial Intelligence" had a surprisingly high number of views and read on Medium.

In the past year, several methods for self-supervised learning of image representations have been proposed. A recent trend in the methods is using Contrastive Learning (SimCLR, PIRL, MoCo) which have given very promising results. However, as we had seen in our survey on self-supervised learning, there exist many other problem formulations for self-supervised learning. Combine clustering and representation learning together to learn both features and labels simultaneously. A paper Self-Labelling(SeLa) presented at ICLR 2020 by Asano et al. of the Visual Geometry Group(VGG), University of Oxford has a new take on this approach and achieved the state of the art results in various benchmarks.

People seemed to enjoy my intuitive and visual explanation of Markov chain Monte Carlo so I thought it would be fun to do another one, this time focused on copulas. If you ask a statistician what a copula is they might say "a copula is a multivariate distribution $C(U_1, U_2, ...., U_n)$ such that marginalizing gives $U_i \sim \operatorname{\sf Uniform}(0, 1)$". I personally really dislike these math-only explanations that make many concepts appear way more difficult to understand than they actually are and copulas are a great example of that. The name alone always seemed pretty daunting to me. However, they are actually quite simple so we're going to try and demistify them a bit.

"excellently delivered step by step .. visually learning is very clear and easily understandable." You'll start the Neural Networks Primer with Fundamentals, Objectives, Data and more: You'll continue the NN Primer with Learning, Backpropagation and Predictions and more topics You'll start the in-Motion section with Inputs, Weights, Biases, Activations, Nodes and Feed-Forward Passes: You'll continue with the in-Motion section with NN Learning, Backpropagation, Tuning and Prediction: You'll finish the in-Motion section by doing a complete rundown on everyting you've learned so far: I will devote a section for more additional knowledge and resources for continous learning. And then, I will conclude with some Final Words. What are some of the Benefits? Lastly, you can post questions or doubts, and I'll answer to you personally.

Machine learning is a type of artificial intelligence program that you can use to give your computer the ability to learn without being completely programmed. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Machine learning focuses deeply on developing computer programs that can change when exposed to new data. In addition to that, ML studies the construction of algorithms and how to make predictions on data.