Learn Deep Learning vs Learn Data Science Tutorials
Price, ratings, monetisation and update history for both apps, side by side — with what reviewers say about each.
Learn Deep Learning
Master deep learning, neural networks, and AI with a comprehensive curriculum. Learn from Python fundamentals to building and deploying advanced AI models, becoming a job-ready AI engineer.
- Deep Learning Fundamentals
- Core AI Architectures (CNNs, RNNs, Transformers)
- Popular Libraries & Frameworks (PyTorch, TensorFlow)
- Computer Vision & MLOps
- Model Deployment & Cloud Infrastructure
- Edge & Mobile AI
- AI Security & Ethics
Read full descriptionHide full description
Master Deep Learning, Neural Networks, and Artificial Intelligence with the most comprehensive learning app. From Python fundamentals to training Large Language Models (LLMs), building Generative AI apps, and deploying models to the cloud — this is your complete path to becoming an AI Engineer. COMPLETE CURRICULUM - Zero to AI Expert.Start from scratch and become job-ready with our structured learning path: Deep Learning Fundamentals: • Introduction to Deep Learning & Neural Networks • How Deep Learning differs from traditional Machine Learning • Artificial Neural Networks (ANN) architecture & training • Loss Functions, Optimizers (Adam, SGD, RMSprop) • Regularization: Dropout, Batch Norm, L1/L2 Core Architectures: • Convolutional Neural Networks (CNNs) for Computer Vision • Recurrent Neural Networks (RNNs), LSTM & GRU for sequences • Transformers: Attention mechanism • Generative Models: GANs, VAEs, Diffusion Models • Autoencoders & Self-Supervised Learning Libraries & Frameworks: • PyTorch: Tensors, Autograd, nn.Module, training loops • TensorFlow & Keras: Sequential, Functional API, model deployment • JAX: High-performance ML research framework • NumPy, Pandas, Scikit-learn, SciPy foundations Computer Vision: • Image Classification with CNNs • Object Detection: YOLO, R-CNN, SSD • Image Segmentation: U-Net, Mask R-CNN • Face Recognition & Facial Landmarks MLOps & Engineering: • ML lifecycle management & experiment tracking • MLflow, Weights & Biases (W&B) for logging • Kubeflow pipelines & Apache Airflow orchestration • CI/CD for Machine Learning • Model versioning, registries & A/B testing • Monitoring, drift detection & model retraining Production & Deployment: • FastAPI & Flask for serving ML models • gRPC for high-performance model inference • Docker containerization for ML apps • Load balancing, caching & request queuing • PostgreSQL, MongoDB & Redis integration • Vector databases: Pinecone, Weaviate, Milvus for RAG Cloud Infrastructure: • AWS SageMaker, EC2, Lambda for ML workloads • Azure Machine Learning platform • Kubernetes (K8s) & Helm for ML orchestration Hardware & Compute • NVIDIA GPUs, CUDA cores & Tensor cores • Neural Processing Units (NPU) & Apple Neural Engine Edge & Mobile AI: • TensorFlow Lite for mobile deployment • Core ML for iOS apps • ONNX for cross-platform model export AI Security & Ethics: • AI safety & alignment fundamentals • Adversarial attacks & robustness • Data poisoning & model stealing defenses • Fairness, bias detection & mitigation Specialized Domains: • Medical AI: Diagnosis, radiology, drug discovery • Robotics: Perception, control, reinforcement learning • Financial AI: Trading, fraud detection, risk modeling • Bioinformatics: Protein folding, genomics • Cybersecurity: Threat detection, anomaly detection Research & Expert Topics: • Foundation models, scaling laws & emergent abilities • Mixture of Experts (MoE) & Sparse Transformers • World models, neuro-symbolic AI • Meta-learning, continual learning, curriculum learning AI TUTOR - Your 24/7 Learning Assistant: • Ask any Deep Learning or AI question • Get explanations of neural network architectures • Debug model training issues with AI help GAMIFIED LEARNING - Stay Motivated: • Daily learning streaks with fire animations • XP points & level progression • Study reminders with push notifications POWERFUL ORGANIZATION TOOLS: • Bookmarks: Save lessons for quick access • Notes: Write personal notes on any lesson • Search: Find anything instantly across all lessons • Dark mode for comfortable night learning LEARN OFFLINE - Anytime, Anywhere: • All content are offline access • Study on your commute without internet PERFECT FOR: • Aspiring AI & Machine Learning Engineers • Data Scientists expanding into Deep Learning • Software developers transitioning to AI • Students studying computer science or AI • Career changers entering the AI field Start your Deep Learning Journey today !
Learn Data Science Tutorials
Master data science with this comprehensive learning app. It covers R programming, SQL, and data visualization fundamentals. Ideal for beginners and those preparing for interviews or tests.
- Data science fundamentals
- R programming language
- SQL curriculum
- Data visualization
- Interview preparation
- Beginner-friendly lessons
Read full descriptionHide full description
Become a complete data science master with this app. Learn the basics of Data Science or become an expert in Data Science with this best Data Science learning app. Learn to code and visualize data for free with a one-stop learning app - “Learn Data Science”. If you’re preparing for a Data Science interview or just preparing for your upcoming test, this is a must have app for you. Data Science Data Science is the area of study which involves extracting insights from vast amounts of data using various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data. R programming R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. - Are you a beginner? Gain the fundamental skills you need to speak the language of data with our data science app. - Start with R programming and begin your data science journey with an in-demand and all-purpose technology. Learn 'R' and become a data science master. This learning path is great for both R programming beginners. - Build up your data analyst skills with our SQL curriculum. In just 5 minutes a day, you’ll become an expert in relational databases and SQL joins, and will learn how to answer a wide variety of data science questions and prepare robust data sets for data analysis in PostgreSQL. - Its time to master the basics of data science with R to get started on the path of exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R. To learn R for data science we covered all aspects as follows: • Introduction • Data-Types in R • Variables in R • Operators in R • Conditional Statements • Loop statements • Loop Control Statements • R Script • R Functions • Custom Function • Data Structures Data science is the practice of mining large data sets of raw data, both structured and unstructured, to identify patterns and extract actionable insight from them. This is an interdisciplinary field, and the foundations of data science include statistics, inference, computer science, predictive analytics, machine learning algorithm development, and new technologies to gain insights from big data. If you like our app. please rate app and share this app to your friends. Thanks.
Screenshots
Verdict
The clearest difference is rating: Learn Deep Learning at 5.0 against Learn Data Science Tutorials's 1.0. Learn Deep Learning also leads on update cadence (every 4 months vs every 15 months). Learn Data Science Tutorials's advantage is price ($1.99 vs $2.99) and iOS requirement (13.0 vs 17). On ratings volume, ads, in-app purchases and monetization model there is nothing between them.
Scored on Price · Rating · Positive reviews · Number of ratings · Update frequency · Ads · In-app purchases · Monetization · Best chart rank · Devices · Requires iOS
Learn Deep Learning costs $2.99 and Learn Data Science Tutorials $1.99 up front. Neither carries in-app purchases, so what you see is what you pay.
Learn Deep Learning holds the better App Store score, 5.0 against 1.0. Both are backed by a comparable volume of ratings — 2 and 1 respectively.
Learn Deep Learning ships an update every 4 months, Learn Data Science Tutorials every 15 months. The most recent releases landed on September 16, 2026 and August 10, 2026 respectively.
| Parameter | Learn Deep Learning | Learn Data Science Tutorials |
|---|---|---|
| Price | $2.99 | $1.99 — better |
| Rating | 5.0 (2 ratings) — better | 1.0 (1 ratings) |
| Positive reviews | — | 0.0% of reviews |
| Number of ratings | 2 — better | 1 |
| Update frequency | Every 4 months — better | Every 15 months |
| Ads | No | No |
| In-app purchases | No | No |
| Monetization | Paid | Paid |
| Devices | iPhone, iPad | iPhone, iPad, iPod — better |
| Requires iOS | 17 | 13.0 — better |
| Further details — not scored | ||
| Size | 188 MB | 41 MB |
| Age rating | 9+ | 9+ |
| Developer | Shahbaz Khan | Muhammad Umair |
Customer experience
Learn Deep Learning
Learn Data Science Tutorials
In-app purchases
Learn Deep Learning
No in-app purchases
Learn Data Science Tutorials
No in-app purchases
Questions
Is Learn Deep Learning free?
Is Learn Data Science Tutorials free?
Which has better reviews, Learn Deep Learning or Learn Data Science Tutorials?
Do Learn Deep Learning or Learn Data Science Tutorials have ads?
Which is updated more often, Learn Deep Learning or Learn Data Science Tutorials?
Other comparisons















