Data Science at Home

In the last episode How to master optimisation in deep learning I explained some of the most challenging tasks of deep learning and some methodologies and algorithms to improve the speed of convergence of a minimisation method for deep learning. I explored the family of gradient descent methods - even though not exhaustively - giving a list of approaches that deep learning researchers are considering for different scenarios. Every method has its own benefits and drawbacks, pretty much depending on the type of data, and data sparsity. But there is one method that seems to be, at least empirically, the best approach so far. Feel free to listen to the previous episode, share it, re-broadcast or just download for your commute. In this episode I would like to continue that conversation about some additional strategies for optimising gradient descent in deep learning and introduce you to some tricks that might come useful when your neural network stops learning from data or when the learning process becomes so slow that it really seems it reached a plateau even by feeding in fresh data.

The secret behind deep learning is not really a secret. It is function optimisation. What a neural network essentially does, is optimising a function. In this episode I illustrate a number of optimisation methods and explain which one is the best and why.

Over the past few years, neural networks have re-emerged as powerful machine-learning models, reaching state-of-the-art results in several fields like image recognition and speech processing. More recently, neural network models started to be applied also to textual data in order to deal with natural language, and there too with promising results. In this episode I explain why is deep learning performing the way it does, and what are some of the most tedious causes of failure.

Artificial Intelligence allow machines to learn patterns from data. The way humans learn however is different and more efficient. With Lifelong Machine Learning, machines can learn the way human beings do, faster, and more efficiently

Talking about security of communication and privacy is never enough, especially when political instabilities are driving leaders towards decisions that will affect people on a global scale

We strongly believe 2017 will be a very interesting year for data science and artificial intelligence. Let me tell you what I expect and why.

Is the market really predictable? How do stock prices increase? What is their dynamics? Here is what I think about the magics and the reality of predictions applied to markets and the stock exchange.

Why the job of the data scientist can disappear soon. What is required by a data scientist to survive inflation.

Data science is making the difference also in fraud detection. In this episode I have a conversation with an expert in the field, Engineer Eyad Sibai, who works at iZettle, a fraud detection company

Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that you must try before further analysis (Part 1).