predict the height given a new age value. In Matlab/Octave, you can load the training set using the commands x = load ( ’ ex1x . dat ’ ) ; y = load ( ’ ex1y . dat ’ ) ; This will be our training set for in addition to the usual x0 = 1, so x ∈ R2 ). If you’re using Mat- lab/Octave, run the following commands to plot your training set (and label the axes): figure % open a new f i g u r e window plot (x line fit from your algorithm on the same graph as your training data according to θ. The plotting commands will look something like this: hold on % Plot new data without c l e a r i n g old p l o t plot

apply distillation in its entirety for a model trained to classify speech commands into one of 12 classes, using the speech commands dataset.The classes Project: Distillation of a Speech Model. The code package for processing audio data, and load the speech_commands dataset from TFDS. !pip install pydub data_ds = tfds.load( name='speech_commands', read_config=tfds.ReadConfig(try_autocache=False) )

P: Average distance traveled before a human-judged error E: A sequence of images and steering commands recorded while ob- serving a human driver Feng Li (SDU) Overview September 6, 2023 11 / 57 Why

Clustering to compress a deep learning model In this section, we will continue to work with the Speech Commands dataset which we used in chapter 3. To recap, it is a dataset of spoken words designed to help train

be enough. Therefore, you should increase the shared memory size by issuing one of the following commands: ‣ --ipc=host ‣ --shm-size= in the command line to docker run --gpus all