For more information, refer to the nvidia-docker documentation. Note: Starting in Docker 19.03, complete the steps below. The method implemented in your system depends on the DGX OS version that you installed 23.07 is available on NGC. Contents of the PyTorch container This container image contains the complete source of the version of PyTorch in /opt/ pytorch. It is prebuilt and installed in the default Python all R418, R440, R460, and R520 drivers, which are not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see

below flow chart: Keras 16 Keras 17 Keras provides a complete framework to create any type of neural networks. Keras is innovative as well as very easy to learn information. The output of one layer will flow into the next layer as its input. Let us learn complete details about layers in this chapter. Introduction A Keras layer requires shape of the input during optimization process. To summarise, Keras layer requires below minimum details to create a complete layer.  Shape of the input data  Number of neurons / units in the layer  Initializers

hyperparameters values. Each trial is configured with an element from the trial set. After all the trials are complete, we pick the one with the best results. The trials are independent of each other which makes them batch(256) tuner.search(tds, validation_data=vds) tuner.results_summary(num_trials=3) Trial 30 Complete [00h 01m 24s] val_accuracy: 0.6313725709915161 Best val_accuracy So Far: 0.7284313440322876 Total

f ′ i (x)ui (2) Let h = 0, then g′(0) = �n i=1 f ′ i (x)ui, by substituting which into (1), we complete the proof. Feng Li (SDU) Linear Regression September 13, 2023 12 / 31 Gradient (Contd.) Definition

results in chaining (clusters can get very large) d(R, S) = min xR∈R,xS∈S d(xR, xS) Max-link or complete-link: results in small, round shaped clusters d(R, S) = max xR∈R,xS∈S d(xR, xS) Average-link:

encoder. Notice how we add a few dense layers after the output of the BERT model. This helps adapt the complete model and be fine-tuned on our task. def get_bert_model( encoder_size, learning_rate=2e-5,