Human motion prediction using RNN

Pro RNN/Python/Math/Algorithm/ Machine Learning/AI Expert! Dear employer Once saw a your project, it was very attracted my mind because I am very interested in your project and also, have rich experiences and high skills on these fields because I have been working in this fields for long time. If given a chance, I am highly confident in my ability to deliver the highest quality. I am confident that my involvement in your project will bring it to a successful launch, on time and within budget. I look forward to hearing from you. Kind Regards

Dear sir. Your project attracted my attention at first glance, because I've extensive experience in Human Motion Prediction Programming. I'm really confident about your project, and very eager to join your project. If we have a chance to cooperate, I'll do my best to provide wonderful result. Looking forward to your response. Best Regards.

Dear sir. I read your project description very carefully. I've really rich experience in developing Human Motion Prediction Program, so your project is very interesting to me. In the past, I developed many projects related on Programming skills. I believe this will be a good starting point of the business relation between us. Thank you.

greetings, i have 3 years of experience in deep learning and can build emotion detector using cnn, rnn architecture. i am graduated from bits pilani ping me for further discussion

Hey, I have worked on Activity recognition as well as Sign language recognition projects using CNN - RNN networks taking in account both the spacial as well as temporal features from the videos/frames. I think I should be able to complete this within 4-5 days efficiently. let me know if you are interested!

I have experience in implementing ML and DL models using tenserflow, keras and scikit-learn. I have completed many projects. Some of these are as: 1. Human Activity Recognition (HAR) :- I have tried three models Support Vector Machines (SVM), K-Nearest Neighbors (k-NN) and Convolutional Neural Network (CNN) to classify six activities(Walking ,Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). The most accurate model tried is SVM with accuracy of 98.47%. 2. Brain Computer Interface(BCI) with the P300 Speller which aims for helping patients incapable to activate muscles to spell words by methods for their brain signal activities. The first classification is to identify the presence of a P300 in the electroencephalogram(EEG). The second classification is the combination of various P300 response for deciding the correct character to spell. 3. Handwritten Digit Recognition using CNN :- Implemented a CNN model using tenserflow to identify handwritten digits from the MNIST dataset. I will first discuss problem statement and your requirement properly and then I will propose an approach and estimated outcomes with you and if you will agree with that then I will code that and complete it. Your requirement and statisfaction will be my first priority. Looking forward to hearing from you. Thank You.