Jibin Mathew

Areas Jibin Mathew is Knowledgeable in:

Techniques Jibin Mathew Uses:

Jibin Mathew's Problem Solving Skills:

1. Machine Learning
2. Deep Learning
3. Recurrent Neural networks
4. Generative adversarial neural network
5. Convolutional Neural Networks
6. LSTM
7. Dynamic Co-attention Networks
8. Classification
9. Regression
10. Predictive Analysis
11. Natural Language Processing/Understanding
12. Chatbots
13. Tensorflow
14. Keras
15. TFLearn
16. EDA
17. Neural Turing Machines
18. Boltzmann Machine

Jibin Mathew's Problem Solving Experience:

1. I created an award-winning Sales Intelligence Platform, which would identify potential customers and predict their buying power, identify right salesperson for your customer, Sales forecasting. accuracy - 91%
2. Solution to classify e-commerce products into classes using Images and their descriptions, which used a combination of Convolutional Neural Networks and LSTMs. accuracy - 93%
3. Built a solution for textiles to identify purity and composition of fabric using deep learning. accuracy of 94%
4. Created a solution to identify human faces and their emotions with retail applications
5. Created generative chatbots, which would learn from a dataset and generate responses
   to queries using a sequence to sequence model.
6. Created a solution to create a Text summarization using Natural Language Processing and Deep Learning
7. Created a sentiment analyzer using RNNs with an accuracy of 90%
8. Created a Lead Generation tool to identify potential lead for an organization matching the vertical the organization wants to focus. accuracy - 98%