Chapter 1 - Introduction to Efficient Deep Learning Welcome to the book! This chapter is a preview of what to expect in the book. We start off by providing an overview of the state of deep learning, its With that being said, let’s start off on our journey to more efficient deep learning models. Introduction to Deep Learning Machine learning is being used in countless applications today. It is a natural automation to search for efficient models and layers. Apart from the above techniques, we gave a quick introduction to efficient models and layers, which are designed with efficiency in mind and can be used as

tokenizer.apply_chat_template() to format your inputs as shown␣ �→below prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant tokenizer.apply_chat_template() to format your inputs as shown␣ �→below prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat-AWQ") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant

............................................................................ iii 1. Keras ― Introduction ............................................................................................. ........................................................................................ 26 Introduction ............................................................................................. makes things simpler.  Keras supports both convolution and recurrent networks. 1. Keras ― Introduction Keras 2  Deep learning models are discrete components, so that, you can

might get superseded by other better methods over time, but again our goal is to give you a gentle introduction to this area for you to be able to research and experiment with these and other similar ideas checkpoints are not directly useful, we hope that this section also provides you with a gentle introduction to self-supervised learning such that you can take a stab at adapting these techniques for your techniques that you can incorporate in your regular model training. The goal was to provide an introduction to the broad themes that you can explore, even if these individual techniques are replaced by

during the training phase, which is the focus of this chapter. We start this chapter with an introduction to sample efficiency and label efficiency, the two criteria that we have picked to benchmark learning continued space limits and high per-message cost meant the practice persisted for some time after the introduction of built-in predictive text assistance despite it then needing more effort to write (and read) continued space limits and high per-message cost meant the practice persisted for some time after the introduction of built-in predictive text assistance despite it then needing more effort to write (and read)

Quantization, a model compression technique that addresses both these issues. We’ll start with a gentle introduction to the idea of compression. Details of quantization and its applications in deep learning follow compression crept into the deep learning field as well. We started this chapter with a gentle introduction of compression using huffman coding and jpeg compression as examples. We talked about footprint

Bidirectionally-Coordinated Net (BiCNet) Neuroscience Hypothesis Source: Reinforcement Learning – An Introduction Architecture Overview https://github.com/alibaba/gym-starcraft Coordinated Moves without Collision

function. We can specify the number and the distribution of contours in the contour function, by introduction different spaced vector, e.g., linearly spaced vector (linspace) and logarithmically spaced vector

Machine Learning[M]. New York: Springer,2006. [5] MINSKY, MARVIN, PAPERT, et al. Perceptrons : An Introduction to Computational Geometry[J]. The MIT Press, 1969. [6] DE Rumelhart, Hinton G E, Williams R

• 剪枝策略的差异：ID3 没有剪枝策略，C4.5 是通过悲观剪枝策略来修正树的准 确性，而 CART 是通过代价复杂度剪枝。 38 参考文献 [1] QUINLAN J R . Introduction of decision trees[J]. Machine Learning, 1986, 1(1):81-106. [2] QUINLAN J R. C4. 5: programs for