Optimization of deep network models through fine tuning

Purpose–Many strategies have been put forward for training deep network models,however,stacking of several layers of non-linearities typically results in poor propagation of gradients and activations.The purpose of this paper is to explore the use of two steps strategy where initial deep learning model is obtained first by unsupervised learning and then optimizing the initial deep learning model by fine tuning.A number of fine tuning algorithms are explored in this work for optimizing deep learning models.This includes proposing a new algorithm where Backpropagation with adaptive gain algorithm is integrated with Dropout technique and the authors evaluate its performance in the fine tuning of the pretrained deep network.Design/methodology/approach–The parameters of deep neural networks are first learnt using greedy layer-wise unsupervised pretraining.The proposed technique is then used to perform supervised fine tuning of the deep neural network model.Extensive experimental study is performed to evaluate the performance of the proposed fine tuning technique on three benchmark data sets:USPS,Gisette and MNIST.The authors have tested the approach on varying size data sets which include randomly chosen training samples of size 20,50,70 and 100 percent from the original data set.Findings–Through extensive experimental study,it is concluded that the two steps strategy and the proposed fine tuning technique significantly yield promising results in optimization of deep network models.Originality/value–This paper proposes employing several algorithms for fine tuning of deep network model.A new approach that integrates adaptive gain Backpropagation(BP)algorithm with Dropout technique is proposed for fine tuning of deep networks.Evaluation and comparison of various algorithms proposed for fine tuning on three benchmark data sets is presented in the paper.

Fine tuning problem of the cosmological constant in a generalized Randall-Sundrum model

To solve the cosmological constant fine tuning problem,we investigate an(n+1)-dimensional generalized Randall-Sundrum brane world scenario with two(n−1)-branes instead of two 3-branes.Adopting an anisotropic metric ansatz,we obtain the positive effective cosmological constantΩeff of order 10−124 and only require a solution≃50−80.Meanwhile,both the visible and hidden branes are stable because their tensions are positive.Therefore,the fine tuning problem can be solved quite well.Furthermore,the Hubble parameter H1(z)as a function of redshift z is in good agreement with the cosmic chronometers dataset.The evolution of the universe naturally shifts from deceleration to acceleration.This suggests that the evolution of the universe is intrinsically an extra-dimensional phenomenon.It can be regarded as a dynamic model of dark energy that is driven by the evolution of the extra dimensions on the brane.

Efficiency-Driven Custom Chatbot Development: Unleashing LangChain, RAG, and Performance-Optimized LLM Fusion

This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Generation(RAG),and enormous language models(LLMs)tweaked with execution proficient strategies like LoRA and QLoRA.LangChain takes into consideration fastidious fitting of chatbots to explicit purposes,guaranteeing engaged and important collaborations with clients.RAG’s web scratching capacities engage these chatbots to get to a tremendous store of data,empowering them to give exhaustive and enlightening reactions to requests.This recovered data is then decisively woven into reaction age utilizing LLMs that have been calibrated with an emphasis on execution productivity.This combination approach offers a triple advantage:further developed viability,upgraded client experience,and extended admittance to data.Chatbots become proficient at taking care of client questions precisely and productively,while instructive and logically pertinent reactions make a more regular and drawing in cooperation for clients.At last,web scratching enables chatbots to address a more extensive assortment of requests by conceding them admittance to a more extensive information base.By digging into the complexities of execution proficient LLM calibrating and underlining the basic job of web-scratched information,this examination offers a critical commitment to propelling custom chatbot plan and execution.The subsequent chatbots feature the monstrous capability of these advancements in making enlightening,easy to understand,and effective conversational specialists,eventually changing the manner in which clients cooperate with chatbots.

基于微调大语言模型的高校AIGC智能客服搭建探索

随着国内外各种免费LLM大语言模型的推出,高校应用大语言模型的基础条件已经具备。利用微调大语言模型实现AIGC高校智能客服,后逐步进入教学与科研场景是比较合适的路径。文章阐述一种可行的方案,如何收集高校客服数据,选择哪种大语言模型,以及数据微调后试用情况。