Very deep learning theough residual networks
*Very deep "plain" networks don't work in practice because they are hard to train due to vanishing gradients. *The skip-connections help to address the Vanishing Gradient problem. They also make it easy for a ResNet block to learn an identity function. *There are two main types of blocks: The identity block and the convolutional block. *Very deep Residual Networks are built by stacking these blocks together
About Me
Do you want a professional team to provide you a complete solution of AI problems?? We are a team of graduate (Ph.D. and masters) professionals in Artificial Intelligence. We have more than 10+ years of expertise in machine learning and deep learning. We are providing complete services related to machine learning, deep learning and data analysis. We have specialized members in every field of deep learning (CV, NLP, RL). Our services includes * Data preprocessing, exploratory data analysis, Implementation of any machine learning algorithm or deep learning problem on MATLAB and python *end to end solution for deep learning problem(CV, NLP, RL) * deployment of ML, DL networks on AWS, GCP or AZURE * customized deep networks for web, mobile, embedded or desktop and app development using services like tflite and intel Openvino Pytorch, tensorflow, Keras, tflite, pandas, matplotlib, pyplot, ggplot, sklearn, SCIKIT , matlab, Open CV, SQL, open AI GYM
$ 66 USD/hr
NEW FREELANCER!
