deep learning一共包含5门子课程,分别是:[神经网络和深度学习]、[改善深层神经网络:超参数调试、正则化以及优化]、[结构化机器学习项目]、[卷积神经网络]和[序列模型]。下面分析[卷积神经网络]课程:The basics of ConvNets部分的习题。 注:为了避免记答案,coursera上的习题每次进去都有所变化,不过大体都是差不多,...
Deep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them ...
as one may expect, there are usually more layers in a deep learning framework than in your average multi-layer perceptron or standard neural network. We have some architectures that are 150 layers deep. Secondly, each layer of a CNN will learn multiple ...
Deep-learning-basics-assignment Assignment 1 In this assignment we learn about the implementation of CNN architecture for classification of MNIST and CIFAR10 dataset. Assignment 2 In this assignment we learn about using autoencoders for cifar10 dataset. Assignment 3 In this assignment we will generat...
CNNs: CNNs typically require fixed-size inputs, which can be a limitation for handling variable-length sequences. Applications of RNNs RNNs are widely used in various fields due to their ability to handle sequential data effectively. Natural language processing Language is a highly sequential for...
预训练和微调在大模型出现之前就已经被广泛应用,比如在CNN领域,很多图像的应用都是用基于Imagenet的CNN预训练模型,比如VGG,Resnet,Inception。在LLM领域,我们现在常常听到OpenAI的GPT,Google的BERT和T5,Meta的Llama,国内清华大学的GLM等都是预训练模型;其他公司可以接入这些模型的API或基于开源模型进行Finetuning。亚马逊...
CNNs, RNNs, GANs, NLP, Recommender Systems, and many more are covered. Furthermore, state of the art models and architectures like Transformers, YOLOv7, or ChatGPT are presented. It is important to me that you learn the underlying concepts as well as how to implement the techniques...
we thought we’d put together a quick introduction to the basics of Machine Learning and how it works. Note: This post is aimed at newbies - if you know a Bayesian model from a CNN, head on over to the research section ofour blog, where you’ll find posts on more advanced subjects....
-Differentiate between various types of machine learning and when to use them. -Implement classification, regression, and optimization techniques in ML. -Utilize deep learning models for complex problem-solving. -Navigate TensorFlow for building and training models. -Explore CNNs and RNNs for image ...
Embrace the cutting edge with YOLO v7, YOLO v8, and faster RCNN, and unleash the potential of pre-trained models and transfer learning. Delve into RNNs and look at recommender systems, unlocking matrix factorization techniques to provide personalized recommendations. Refine your skills in model ...