A few months ago, I overcame a challenging hurdle in my spare time passion project. It seemed like I reached a dead end, but I got past it thanks to discovering a new Python package, SymPy. The Background After going down the rabbit hole with reinforcement learning, I conceded my problem was best solved using a regression algorithm. Still, the best results I saw came from exploiting the training loop in the early days. ...
Reinforcement learning is my area of focus so far when it comes to deep learning. RL is probably not the first stop for most on the machine learning education circuit, but it just happens to be where the problem I am trying to solve has taken me. When starting down the RL road, I was immediately confused by the concept of reward. Nothing was confusing about what it was - giving a dog a treat when it sits after you’ve asked it to sums things up pretty well. Instead, I was confused by its relation to the loss function. Both, at a high level, help inform when the correct thing is happening. ...
My journey into machine learning is still in its relatively early stages. I began by familiarizing myself with Tensorflow. It was an excellent introduction, whetting my appetite for AI, but I am now ready to make the switch to PyTorch. There are two key drivers behind my decision to make this change. Debugging Process transparency Debugging Debugging in Tensorflow has been problematic, to say the least, being most painful when writing custom loss functions. I’d feed dummy data to the loss function while writing it, pre-vetting what I was creating. However, when something didn’t work as expected during training, troubleshooting was near impossible. Tensors at training time are opaque, acting in some “virtual” fashion if my memory serves (this was a while ago now). ...
I have been dabbling in deep learning recently, and I have completed a few courses and read through and watched many resources during that time. It can be hard to find good help, so I wanted to share my experience with Kaggle quickly. If you’re not familiar with Kaggle, it is an excellent source of datasets for training your models. I believe this is what they are most well known for. They also have a complete set of courses relating to many things data science-related. The topics include data visualization, how to use the Pandas library, and more. ...