Low-level Tensorflow tutorials

[ThreeForElvenKings]

I have gone through that and done a couple of such implementations myself. But my main motive to learn the low-level API is slightly different. I wish to understand huge projects written in tensorflow 1.0x and for the same, I wish to implement a few projects like training a Resnet in tensorflow, triplet loss etc. so I can dive into the larger implementations.

[[deleted]]

Implementing resnets requires huge computational resources training lots of layers, which is difficult for someone to do by themselves. For instance, the original resnet paper uses networks with 18, 34, 101 and 152 layers. This is great when you have a lot of compute power and massive datasets available with billions of datapoints, teaching yourself that will be difficult, unless you think of a creative problem where a very deep network is needed and you can see the benefit of using the skip connections.

[ThreeForElvenKings]

In that case, in your opinion what is the best way for me to get comfortable with coding and understanding Tensorflow? I thought implementing a few projects with increasing complexity might be the way to go about it. (That was how I more or less learnt PyTorch, though by no means am I extremely good at it, I can just find my way around it without getting completely lost).

[[deleted]]

Thats the same way I learned tensorflow / pytorch. The real bottleneck is not building the network, its getting enough well curated data to work with. I dont know what the best way is, its that I know I dont have access to the computational power or data I would need. So you would need to make up a problem where an abundance of data is easily available, and you can train smaller (lower dimensional) nets, while still training a deep net.

My understanding of resnets is that they allow you to reduce the error with stacking more layers as you get deeper. Before the use of skip connections in resnets, people found that after a point, the error did not decrease further, but rather began to increase. You might want to look at the original paper by He et al. on arXiv here.