2.9x tensor cores , 2.8x cuda cores.

I used mix precision training, should have been largely fp16. But you can input as float32. Pytorch amp will auto cast to fp16. I only get 2x speed more with 3090.

Only 2×more than 3060. May be you are more power limited or CPU bottle necked when using both GPUs, or PCEi bandwidth limited.

You need to buy credit now to use colab.

Colab is not free anymore except for very short time.

Buy 3060 12GB. 3070 8GB vram has more limitations. Colab is largely not free now. It is fine you are willing to pay for the service. You can also use vast.ai and lambda labs for cloud GPU.

Whatever you can read are outdated. They don't reveal what they actually use. They are rumored to have the best recommendation system.

The best way to learn is to go through tutorials from various source just to get a feel of what's like to do ML and DL. Then go through the theory including data pipeline, target, models, loss function, gradient, optimizer etc.

Pytorch or Jax. Tf is on the way out.

Machine learning is more framework. The knowledge is more important. Like, why does batch normalization cause problem ? how do you increase model performance without change model architecture? Which optimizer gives better performance SGD vs Adam ?

It depends. For colab, only highest tier pro account allow you to turn off the computer. The machine still runs at GCP. Different providers have different setups. I think you don't have to leave browser open if the machine is just port forward to your local machine port. The best is to contact your provider about your instance.

3090 is not a good card, running at high temperatures, high noise, excessively high VRAM temperatures.

You save a lot electricity cost. Much beter value if you plan to use it a lot. It"s much easier to manage 2 cards than 4 cards.

Buy 2× 4090

JAX is the new toy,. It's not customer framework.

Great, except I switched to Android.

Rtx 30070 or waiit for 4070. Then a laptop with good cooling system. Usually a gaming laptop with many holes in the botton and two fans inside , that put air out in the front or sides of the laptop. Then, make linux OS drive at the second SSd slot and have 32 GB or more RAM.

Model parallelism. But you need more than 1 card. Buy A6000 which has 48 GB vram.

A lot of Stanford classes are free on youtube. They are probably among the best. Andrew Ng coursera classes are modified from his Stanford class. Most of math are not difficult, linear algebra, caculus and some statistics like maximum likelihood etc. Math can be more difficult if you want to study some branches of deep learning. The goal is to establish approximate functions with deep learning which is stacking up basic simple units into multiple layers of a deep network.

It's not false positive. The models were trained with pictures, not far from cartoons. I think models performed really well.

Use convnet to transform to the right shape. May need to use dilution.

Use convnet to transform to the right shape

Convnext is really good, and computation efficient.

A desktop with 3090 is much better and cheaper. Buy a big case with good airflow. Or go for 4090.