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Choudhary Nouman M.

4.8

6.1

94%

Wah cantt, Rawalpind, Pakistan

$20 USD per hour

Hello I am Nouman Choudhary, a skilled eCommerce brand Manager and founder of AMZFBAConsultant. With more than 8+ years of experience in the industry, I have sharpened my skills in different areas to offer nothing but exceptional services to my clients. I am also working as an Amazon seller central and vendor central expert with an array of clients from different regions. My portfolio includes the experience of different Amazon cases and other eCommerce service specializations. I use only premium paid tools for offering services to my clients. My service list includes ● PPC Advertisings Campaigns Management & Optimization ● Teikametrics, Sellics & Helium10 Specialist ● PL Product Research & Product Listings using bulk feed file Operations for Variations etc ● Keywords Research & Top Competitors Analysis & Reverse Engineering ● Premium Enhanced Brand Content (EBC) ● Copywriting & HTML Designed Description ● Shopify Plus Stores Liquid Coding ● CSS PhP websites coder ● Laravel Custom websites ● Shopify store enhancements ● Custom Plugins & delegalization ● Ruby Rail Websites Coding ● Shopify (CRO) Conversion Rate Optimization ● HTML Landing Pages websites ● WordPress websites Optimization and Responsiveness ● Google ADs ● Meta ADs & Social Media Advertising ● Package designing ● Photoshop editing ● Infographics images ● lifestyle images ● USPTO, WIPO, and UKPTO Brand Registry Issues & Registration The premium tools that I use for providing services include Helium10, Jungle Scout, Viral Launch, Merchant Words, AMZ Scout Pro, FBA Calculator, and more. I strive to create long-term relationships with my clients by offering the highest-quality work in the mentioned time frame. For big projects and services, I lead a team of 5 individuals who are well-qualified and experienced. All the services and budget are negotiable. I would love to grow your business to new heights. Gratitude. Nouman Choudhary

Long Short-Term Memory Network PHP Python Website Design Graphic Design

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Hani S.

5.0

100%

Cairo, Egypt

$20 USD per hour

I enjoy working in many disruptive fields such as data analysis, machine learning, deep learning, computer vision, and Trading. Also, I have extensive EPC / EPCM experience working in large infrastructure projects in Engineering, Estimations, Analysis, Commissioning, Startup, Functional Management, Project Management, and Business Development. Experience and roles accumulated over more than twenty years. I extended my capabilities to include AI implications to business strategies where I also provide consultations to AI deployment in Business. My technical capabilities can be summarized as follows: 1. Python programming using data science and AI packages such as NumPy, pandas, matplotlib, seaborn, PyTorch. 2. Computer Vision using opencv 3. Trading using zipline and alphalens 4. Data analysis: Applying formal analysis, seeing the unseen, dashboarding, predicting, and estimating 5. Microsoft Office, VBA: Normal application use with advanced capabilities like functions, layouts as well as programming using the related VBA. 6. Extensive knowledge of C, C++, Visual C, J2ME. 7. Microstation VBA: Although I am not using Microstation for modeling or drafting, I used its VBA capabilities to automate modeling and drafting.

Long Short-Term Memory Network Python SQL Machine Learning (ML) C++ Programming

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Vety Bhakti L.

5.0

0.0

100%

Daerah Khusus Ibukota Jakarta, Indonesia

$15 USD per hour

I'm a data scientist with approximately two and a half years of experience working in both consulting and marketplace environments. My expertise spans several key areas, including: Data Processing & Analysis - Data cleansing and manipulation - Exploratory data analysis (EDA) - Data visualisation Machine Learning & Advanced Analytics - Building machine learning and deep learning models - Computer vision Tools & Technologies - Python, SQL, Amazon Redshift, Google BigQuery - Microsoft Excel, Google Spreadsheet - KNIME, Looker Studio, Power BI, Tableau, Amazon QuickSight Development & Deployment - Streamlit, FastAPI, and Docker

Long Short-Term Memory Network Data Processing Data Entry Excel Data Analytics

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Dhinesh B.

5.0

0.0

100%

chennai, India

$15 USD per hour

I have a skill in Power BI, Machine Learning, Deep Learning, Data Science, and Data Analytics with practical knowledge.

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Bhanupratap P.

5.0

1.0

100%

Gotegaon, India

$20 USD per hour

Machine learning, Statistics,NLP,Deep Learning,Large Language Models are the core competencies.Love to finding pattern,trend and make a judgement based on the data.I am a mechanical engineer so can do mechanical domain specific work very efficiently with quality.

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Janice R.

4.8

0.0

100%

Aliwal North, South Africa

$15 USD per hour

I help businesses save time, increase productivity, and grow by streamlining operations through professional virtual assistance, business automation, and modern web solutions. Whether you need a reliable Virtual Assistant, a custom web application, a high-performing WordPress website, CRM management, data entry, or workflow automation, I deliver accurate, scalable, and results-driven solutions—on time and with clear communication. Why clients choose me: * ✅ 7+ years of customer service and business operations experience * ✅ 99% data entry accuracy with exceptional attention to detail * ✅ Built 100+ automation workflows to eliminate repetitive tasks * ✅ Handled 5,000+ customer interactions across multiple channels * ✅ Extensive experience managing logistics and business operations * ✅ Developed professional websites and custom business solutions * ✅ Committed to delivering high-quality work, meeting deadlines, and exceeding client expectations My goal is simple: help you work smarter, save valuable time, and focus on growing your business while I take care of the technical and operational details.

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How to hire a great freelance Long Short-Term Memory Network Engineer

A Long Short-Term Memory Network Engineer designs, trains, and deploys LSTM-based recurrent neural networks that learn from sequential data to forecast trends, classify patterns, and generate predictions across time-series and language tasks.

What does a Long Short-Term Memory Network Engineer do?

An LSTM engineer specializes in a class of recurrent neural networks built to handle long-range dependencies in sequence data. Unlike standard feedforward models, LSTMs use memory cells and gating mechanisms to retain context across hundreds or thousands of time steps, which makes them well suited to forecasting, anomaly detection, speech recognition, and natural language processing.

A freelance LSTM network engineer translates raw sequential data into production-ready predictive models. They handle the full pipeline: data preparation, feature engineering, architecture design, hyperparameter tuning, evaluation, and deployment. The commercial value is concrete — better demand forecasts, sharper fraud detection, more accurate predictive maintenance, and language models that capture nuance traditional methods miss.

Core deliverables from an LSTM engineer

Projects vary by use case, but the typical scope of work an LSTM specialist delivers includes:

Time-series forecasting models for sales, demand, energy load, stock prices, or sensor readings

Sequence classification systems for sentiment analysis, intent detection, or activity recognition

Sequence-to-sequence models for machine translation, text summarization, and chatbot response generation

Anomaly detection pipelines for IoT telemetry, network logs, or financial transactions

Speech-to-text and audio classification models built on stacked LSTMs or BiLSTMs

Predictive maintenance systems that flag equipment failure from vibration or temperature sequences

Data preprocessing pipelines including normalization, windowing, padding, and tokenization

Model evaluation reports covering RMSE, MAE, MAPE, F1, perplexity, and confusion matrices

Deployment-ready artifacts: serialized models, REST or gRPC inference APIs, and monitoring dashboards

Tools, frameworks, and methodologies

Strong candidates work fluently across the modern deep learning stack. Expect proficiency with:

TensorFlow and Keras for building and training LSTM, BiLSTM, and stacked LSTM architectures

PyTorch and PyTorch Lightning for custom recurrent models, attention layers, and research-grade work

scikit-learn for baseline models, preprocessing, and cross-validation

pandas and NumPy for sequence preparation, lag features, and rolling-window engineering

CUDA and cuDNN-accelerated training on GPU instances

MLflow, Weights and Biases, or TensorBoard for experiment tracking

Docker, Kubernetes, AWS SageMaker, Google Vertex AI, and Azure Machine Learning for deployment

ONNX and TensorFlow Lite for model export and edge inference

A good engineer will also know when an LSTM is the right tool — and when a Transformer, GRU, Temporal Convolutional Network, or classical ARIMA model would serve the problem better. That judgment matters as much as the implementation skill.

Industries and use cases

LSTM models show up wherever order matters in the data. Common industries hiring LSTM expertise include:

Finance — algorithmic trading signals, credit risk scoring, fraud detection on transaction streams

Retail and e-commerce — demand forecasting, inventory planning, customer churn prediction

Healthcare — patient deterioration prediction, ECG and EEG signal analysis, clinical NLP

Energy and utilities — load forecasting, smart-grid optimization, renewable output prediction

Manufacturing — predictive maintenance, quality control from sensor streams

Telecommunications — network anomaly detection, traffic forecasting

Media and tech — speech recognition, language modeling, recommendation systems with sequential signals

How to evaluate LSTM engineering candidates

Strong LSTM engineers usually combine a quantitative background — computer science, statistics, electrical engineering, or applied mathematics — with hands-on deep learning experience. Look for portfolio evidence of end-to-end projects, not just model notebooks. Code on GitHub, published papers, Kaggle competition results, and shipped production systems are all strong signals.

Portfolio markers worth checking include training curves and validation strategy (not just final accuracy), use of proper time-series cross-validation, evidence of hyperparameter search, and clear documentation of data leakage controls. Ask to see how they handled non-stationary data, missing values in sequences, and class imbalance.

Sample interview questions you can use directly:

How do LSTM gates solve the vanishing gradient problem that plagues vanilla RNNs?

When would you choose a GRU or Transformer over an LSTM, and why?

How do you prevent data leakage when building features and validating a time-series LSTM?

Walk me through how you would tune sequence length, batch size, and dropout for an LSTM forecasting model.

How would you deploy a trained LSTM for low-latency inference, and how would you monitor it for drift in production?

Why hire Long Short-Term Memory Network engineers on Freelancer.com

Freelancer.com gives you direct access to a global pool of machine learning engineers, deep learning researchers, and data scientists with proven LSTM experience. You can review portfolios, published work, ratings, and verified reviews before you shortlist anyone. The platform supports both fixed-price and hourly engagements, so a quick proof-of-concept and a multi-month production build are both well supported.

Clients on Freelancer.com set their own budgets and receive competitive bids from freelancers worldwide, which means you can match scope to budget rather than the other way around. Milestone Payments hold funds in escrow and release only when you approve deliverables, giving you control over quality at every stage. Whether you need a one-week forecasting prototype or a full deployment with monitoring, you can hire on Freelancer.com with confidence.

Post a project on Freelancer.com today and connect with skilled Long Short-Term Memory network engineers ready to build, train, and deploy production-grade sequence models for your business.

What are the steps to hiring a freelance Long Short-Term Memory Network engineer?

Hiring an LSTM engineer works best when you treat the brief as a technical specification, not a wish list. The clearer you are about your data, target metric, and deployment environment, the better the bids you will receive. The three steps below walk you through posting, reviewing, and awarding the project.

Step 1: Post your project on Freelancer.com

Your project post is the single biggest determinant of bid quality. A precise brief filters out generic ML applicants and attracts engineers whose LSTM experience genuinely matches your problem. Head to the post project page , fill in the brief, scope, timeline, and budget, then publish the project for freelancers to bid on.

Describe the data: number of sequences, sequence length, sampling frequency, feature dimensions, and label availability

State the target metric and acceptable performance threshold (RMSE, MAPE, F1, or domain-specific KPI)

Specify the framework preference if you have one — TensorFlow, Keras, or PyTorch

Note deployment constraints such as cloud platform, latency targets, edge requirements, or batch versus streaming inference

List deliverables: trained model artifact, training code, evaluation report, inference API, documentation

Include timeline expectations and any compliance or data-privacy requirements

Step 2: Review bids from freelancers

Bids are short proposals, not just price quotes. They reveal how each freelancer interprets your brief, what architecture they would try first, and what timeline they consider realistic. Read the proposals carefully and shortlist candidates whose technical reasoning matches the problem.

Look for proposals that reference specific architectures (stacked LSTM, BiLSTM, encoder-decoder with attention) and justify the choice

Check whether the freelancer raises clarifying questions about your data, validation strategy, or success metric — that signals genuine engagement

Confirm timeline realism: training, tuning, and deployment cannot be compressed indefinitely

Review attached samples — past LSTM projects, GitHub repos, Kaggle notebooks, or published work

Use Freelancer.com chat to ask follow-up technical questions before shortlisting, such as how they would handle non-stationarity or class imbalance in your data

Step 3: Evaluate freelancers and award the project

The final decision combines proposal quality with profile evidence. Look at portfolio depth, client reviews, and consistency across past work — a single brilliant project matters less than a track record of completed deep learning engagements. Weigh ratings alongside written feedback to understand how the freelancer communicates and handles revisions.

Review portfolio depth: prior LSTM, RNN, sequence modeling, and time-series projects relevant to your domain

Check star ratings and the volume of completed projects on Freelancer.com

Read written client reviews for signals on communication, code quality, and adherence to deadlines

Confirm verified payment method, completion rate, and on-time delivery rate

Look for evidence of production deployment experience, not just notebook prototyping

Award the project and set up Milestone Payments so funds are released only when each deliverable meets your acceptance criteria

What is the difference between an LSTM engineer and a general machine learning engineer?

A general ML engineer covers a broad toolkit including tree-based models, linear methods, and clustering. An LSTM engineer focuses on sequential and time-dependent data, with deep expertise in recurrent architectures, sequence preprocessing, and the engineering tradeoffs specific to training RNNs at scale.

Should I hire an LSTM engineer or a Transformer specialist?

It depends on your data and constraints. Transformers often outperform LSTMs on long-context language tasks but require more data and compute, while LSTMs remain efficient and strong for moderate-length time series, streaming inference, and edge deployment. A good freelancer will benchmark both and recommend the right architecture for your problem.

How long does an LSTM project typically take?

A focused proof-of-concept on clean data can take one to three weeks, while a production-grade pipeline with deployment and monitoring usually runs six to twelve weeks. Timelines depend heavily on data quality, labeling needs, and integration requirements.

Can I hire an LSTM engineer for a one-off project?

Yes. Many clients post a project on Freelancer.com for a single forecasting model, anomaly detection prototype, or research replication. You can scope the engagement to a defined deliverable and extend it later if you want production deployment or ongoing model maintenance.

Do I need to provide my own data?

In most cases, yes — LSTM models require historical sequences specific to your domain. A skilled freelancer can advise on data volume requirements, help with synthetic data augmentation, or recommend public datasets if your own data is limited.