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Ahmad Wafri C.

@ahmadwafri

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5,0
7
3,6
3,6
57%

Top AI & Full-Stack

$30 USD / godz.
・
Flaga
Malaysia (9:23 PM)
・
Dołączył/a czerwca 7, 2023
$30 USD / godz.
・
I'm working on AI technologies and web development such like NLP, ASR, CV, full stack and so on for long years. I've expertized to several AI model building/deploying with extensible, scalable functionalities. Top skills are ChatBot, NER, RE, QA and Stock AI, Speech Analysis, Computer Vision AI, full stack. For 13 years Iive spent programming (8 as primary income) Iive been cultivating persistence, performance, and a love for challenges, which allows me to consistently deliver complex tasks that others might give up. Of course, programming is always a learning experience and I am to this day doing that.
Weryfikacje
Na czas
100%
W ramach budżetu
100%
Zaakceptuj stawkę
70%
Freelancer Python Developers Malaysia
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Portfolio

Portfolio

12028006
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
11156463
- Utilize several cloud API for STT (Azure, AWS, GCP, SpeechBrain, etc) - Custom service based on trained model.
Speech to Text service
11156454
Chatbot can read various document type and answer to the question based on understanding from document content. It is capable to read docx, pdf, csv and webpage as of now.
Document readable Chatbot development
11156448
The service running on AWS will be high scalable, extendable, efficient by following this pipeline.
AWS service architecture
12028006
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
In this notebook, I built and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend. Even though a hyperparameter tuning mechanism is out of the scope of this notebook, I have tried a series of networks such as: Dense, Convolutional with and without Max Pooling, Long Short-Term Memory (LSTM), Gated Recurrent Unit, etc. The metrics I was using to evaluate how well our model generalizes when predicting on unseen data are MAE (Mean Absolute Error) and MSE (Mean Squared Error). The Neural Network that scores the lowest loss is the one that should be deployed to production. It is also important to mention that this model will be trained based on the moving averages as the Bitcoin's price is full of noise, and I don't want our model to learn useless patterns
Cryptocurrency price prediction using LSTMs
11156463
- Utilize several cloud API for STT (Azure, AWS, GCP, SpeechBrain, etc) - Custom service based on trained model.
Speech to Text service
11156454
Chatbot can read various document type and answer to the question based on understanding from document content. It is capable to read docx, pdf, csv and webpage as of now.
Document readable Chatbot development
11156448
The service running on AWS will be high scalable, extendable, efficient by following this pipeline.
AWS service architecture
Zmiany zapisane
5.0 · 7 Reviews
Oceny
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تطوير موقع تجارة شحن الألعاب والتطبيقات • $100 USD
لا يستطيع العمل الا بارسال الاموال اليه مقدما حقا اظن انه محتا
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Worst freelancer ever! wasted me a week of time, he cannot do anything! probably just take the project and give to another freelancer to do, it's just a simple html page, and replace some contents in it, he cannot complete and deliver the work, keep letting me wait, I should aware that's a negative sign, dont trust this guy, he will literally waste all your time until deadline, avoid!!!
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@saasprivacy
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Image Score Derivation and Selection Tool • $50 SGD
Ahmad has completed the project within timeline and met the requirements, his skill set on Python is extremely good.
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first topic • $50 USD
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@Diane40
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Flaga
Oak Point, United States
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7 miesięcy temu
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Create a specific Digit Recognition to detect date and time • ₹26 000 INR
He is good and have knowledge about the work and technologies he is working. He helped me with image processing, Open CV for time detection task in the image.
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@sukumarreddy6
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