Install Docker. ‣ For NVIDIA DGX™ users, see Preparing to use NVIDIA Containers Getting Started Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation that you have access and can log in to the NGC container registry. Refer to NGC Getting Started Guide for more information. The deep learning frameworks, the NGC Docker containers, and the deep learning examples can be found here. For more information about AMP, see the Training With Mixed Precision Guide. Tensor Core Examples The tensor core examples provided in GitHub and NGC focus on achieving the

192 Training a Unoptimized Model We are all set to start training our model. Tensorflow provides a user-friendly API to train the model. All we need is to invoke the fit() method on the model object. It languages (like Java for Android or C++ for iOS and other platforms) for inference. The authoritative guide for TFLite inference is available on the tensorflow website. def tflite_model_eval(model_content As mentioned earlier, the tflite evaluation is a boiler-plate code. You can refer to the TFLite guide for more details. We start the model conversion by creating a converter object using the from_keras_model()

language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True 5-7B-Chat", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."} ], }' 或者您可以按照下面所示的方式，使用 openai Python model="Qwen/Qwen1.5-7B-Chat", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."}, ] ) print("Chat response:", chat_response)

com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-1- autoencoder-d9a5f8795af4 How to Train? PCA V.S. Auto-Encoders Adversarial AutoEncoders ▪ Distribution of hidden code https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Adversarial Adversarial AutoEncoders ▪ Give more details after GAN https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Another Approach:

well as time-consuming. However, as initial pointers you can refer to this guide for pre-training BERT in Keras, and this guide for some optimizations to make it efficient. Also consider going through the

effects of transformations visually. The above list is not exhaustive, rather we have used it as a guide to help make better transformation choices. A few other commonly used techniques are contrast augmentation family to decide whether it is a good decision. We rely on their perspectives and life experiences to guide us through the process. Similarly, when ensembling we hope that each individual model would learn

Convolution Moving window Several kernels Animation https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural- networks-260c2de0a050 Notation Input_channels: Kernel_channels: 2 ch Kernel_size:

edu/spring17/lec01_cnn_architectures.pdf Receptive Field https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural- networks-260c2de0a050 Weight sharing ▪ ~60k parameters ▪ 6 Layers http://yann

digits have been size- normalized and centered in a fixed- size image. Fashion MNIST dataset This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images

process in last year's lecture video. Training & Testing Neural Networks Validation Testing Training Guide for training/validation/testing can be found here. Training & Testing Neural Networks - in Pytorch