### What is the Benefits of Contour Annotations While Creating Image Training Data Sets for AI or ML?

Image data sets annotated with Contour annotations definitely have some benefits. Actually, this method of annotation allows you to place contour labels on the contour plots and represent the numerical value of entire contour line. Contour Annotations Benefits for Image Data Sets The best part of this tool is output of this tool is a…

### What are some new deep learning models for learning latent representation of data?

I know that autoencoders are one type of deep neural networks that can learn the latent representation of data. I guess there should be several other models like autoencoders. What are some new deep learning models for learning latent representation of data?

### Hyperparameter optimisation over entire range or shorter range of training episodes in Deep Reinforcement Learning

I am optimising hyperparameters for my deep reinforcement learning project (using PPO2, DQN and A2C) and was wondering: Should I find the optimum hyperparameters to get maximum reward from training over my entire range of training (e.g. 50 million steps) or can I optimise over less time (e.g. 1 million steps)? What is the conventional…

### Do all neurons in a layer have the same activation function?

I’m new to machine learning (so excuse my nomenclature), and not being a python developer, I decided to jump in at the deep (no pun intended) end writing my own framework in C++. In my current design I have given each neuron/cell the possiblity to have a different activation function. Is this a plausible design…

### How do we get to the f(W) to calculate the gradient of f(W)?

As we know, we have such a formula in deep learning: f(W) = loss_value W(i) = W(i-1) – alpha * gradient(f(W)(i-1))) So, how do we get to the f(W) to calculate the gradient of f(W)? As an example, we can initialize the set of W to 0.5 . How can you explain it to me?

### How to understand my CNN’s training results?

I created a multi-label classification CNN to classify chest X-ray images into zero or more possible lung diseases. I’ve been doing some configuration tests on it and analyzing its results and I’m having a hard time understanding some things about it. First of all, these are the graphs that I got for different configurations: Results…

### Is it possible to combine multiple SVMs that were trained on sublayers of a CNN into one combined SVM?

I have created a CNN for use on the MNIST dataset for now (so I have 10 classes). I have trained SVMs on the sublayers of this trained CNN and wish to combine them into a combined SVM as to give a combined score. So far, I trained two individual SVMs at two of the…

### 2020 is fast approaching: what changes should a book like AIMA contain to be more up to date?

Just found out that the book “Artificial Intelligence: A Modern Approach” the fourth edition will be released in April 2020. The book is a major reference for anyone that wants to get in the field of AI. The field has indeed grown a lot in recent years, which is why a revision was planned. According…

### Value of PAC learning

I was wondering, what is the practical value of things like PAC learning and VC dimension? To my understanding there are two hits against these theories. The first is that the results all are conditioned on knowing be appropriate models to use, ex what degree of complexity. The second is that the bounds are very…

### What is the term for an RNN that is a completely connected directed graph?

There seems to be a severe problem with the taxonomy of neural network topologies. What I’d like to know is the term I should use to search for the most general topology: completely connected directed cyclic graph (henceforth CCDCGRNN). This is because all other topologies degenerate by constraint from CCDCGRNN. This includes topologies that are…