Drone Uses AI and 11,500 Crashes to Learn How to Fly
This is interesting. It really shows how deep learning has become almost &quot;Lego-like&quot;.<p>1. Imagine the problem, add a camera or two (or more).<p>2. Build&#x2F;use a pre-trained ImageNet model as a starting point (probably using TensorFlow&#x2F;Keras).<p>3. Build a dataset, split it into test, train, validation sets.<p>4. Train the model further.<p>5. Test and validate the model. Lower the error rate (don&#x27;t overfit though!).<p>6. Profit?<p>As far as what language to use, depending on the speed of whatever you&#x27;re trying to do, Python would likely work fine in the majority of cases. If you need more than that, C&#x2F;C++ is around the corner.<p>Oh - and OpenCV or some other vision library will probably be used (but just to grab the images, maybe a little pre-processing).<p>You wouldn&#x27;t have to use this exact pipeline (you could substitute other deep learning libs, other vision libs, other languages, etc) - but the basics are to start with a well-known CNN model, preferably &quot;pre-trained&quot;, then apply your own dataset(s) to the task to get it to work better. Not much more tweaking needed, the biggest thing is to get (or be able to synthesize from what you do have) enough data to throw at it (and have a fast enough system to train it in reasonable time).<p>We&#x27;ve seen this approach many, many times; it seems to work well for a ton of domains and problems. Again - very &quot;Lego-like&quot;...