Part 7: Quantising our graph In our previous tutorial we produced our frozen model so now we can optimise it to make it run on our FPGA hardware efficiently, which we can do through quantisation. Quantisation is the process of
Part 6: Converting and Freezing our CNN Now we have built a more optimal CNN by handling both under-fitting and over-fitting, we can begin the process of deploying our model on the FPGA itself. The first step in this process
Part 5: Optimising our CNN In our previous section, we both trained our network on a training set and tested it on a testing set and our accuracy on the training set (0.972) was higher than on our testing set
Part 4: Training the neural network Welcome to Part 4 of our tutorial where we will be focused on training the neural network we built in the previous section Introduction Getting Started Transforming Kaggle Data and Convolutional Neural Networks (CNNs)
Part 3: Extracting Kaggle data and building the Convolutional Neural Network (CNN) Welcome to Part 3 of our tutorial where we will be focused on how to extract our data from the Kaggle set and building our Convolutional Neural Network.
Part 2: Introducing Sign Language MNIST Welcome to the second part in out tutorial in using Vitis AI with Tensorflow and Keras. Other parts of the tutorial can be found here: Introduction Getting Started (here) Transforming Kaggle Data and Convolutional
Part 1: Introduction This the first part in our multi-part tutorial on using Vitis AI with Tensorflow and Keras. Other parts of the tutorial can be found here: Introduction (here) Getting Started Transforming Kaggle Data and Convolutional Neural Networks (CNNs)