LearnML vs Machine Learning+

Learn how a neural network works by building and training one yourself. LearnML takes you through four steps: Data, Model, Train and Try it. START WITH A WORKING EXAMPLE Make your first prediction with a ready-to-use even-or-odd model. Short explanations show what each step does and what to try next. LEARN THROUGH THREE GUIDED LESSONS • Classify numbers as even or odd. • Predict a temperature from previous values. • Explore image classification with animal photos. The temperature examples are simulated learning data, not a weather forecast service. BUILD AND EXPERIMENT Choose number, vector or image inputs. Edit examples, select the correct answers and build a model from dense, convolution, pooling, activation and dropout layers. Start with a matching model or change its architecture and training settings. TRAIN ON YOUR DEVICE Watch progress and compare training and test metrics. Stop a run when you need to. Try a new number, set of values or photo after training. Predictions can be wrong: use them to explore what the model learned. KEEP YOUR WORK Experiments, examples, trained models and lesson progress are saved locally between launches. Rename and duplicate experiments, keep successful training runs, or export and import an experiment as a JSON file. Interrupted training can be started again from the restored experiment. MADE FOR IPHONE AND IPAD Follow one step at a time on iPhone. Use the sidebar on iPad and view a lesson's explanation beside its practice area when space allows. English and Russian interfaces are included, with support for larger text and improved accessibility controls. No account or cloud training is required. Choose your own photos or use the built-in examples.

Master all essential topics in Machine Learning exam with Fun and Engaging Quizzes! Dive into the world of Machine Learning with our comprehensive quiz app, designed to boost your knowledge, confidence, and skills. Whether you're a student, practitioner, or just exploring the field, this app is your ultimate companion for learning and growth. Topics Covered: Introduction to Machine Learning: -Definition, scope and applications in engineering domains -Types of machine learning (supervised, unsupervised, reinforcement) Mathematical Foundations: -Linear algebra essentials -Probability and statistics -Calculus for optimization Data Engineering for ML: -Data collection, cleaning, and preprocessing -Feature engineering and selection -Handling missing and imbalanced data Supervised Learning Algorithms: -Regression models -Classification techniques -Evaluation metrics Unsupervised Learning Algorithms: -Clustering methods (k-means, DBSCAN, hierarchical) -Dimensionality reduction (PCA, t-SNE) -Applications in anomaly detection Neural Networks and Deep Learning: -Perceptrons and MLPs -Activation functions -Backpropagation Advanced Deep Learning Architectures: -Convolutional Neural Networks (CNNs) -Recurrent Neural Networks (RNNs), LSTMs, GRUs -Transformers and attention mechanisms Reinforcement Learning: -Markov decision processes -Value-based methods (Q-learning) -Policy-based methods Model Training and Optimization: -Gradient descent and variants (SGD, Adam, RMSProp) -Hyperparameter tuning -Regularization techniques Model Evaluation and Validation: -Cross-validation methods -Bias-variance trade-off -Overfitting and underfitting ML Engineering and Deployment: -Model pipelines and MLOps -Deployment strategies (cloud, edge, embedded systems) -CI/CD for ML Scalable Machine Learning: -Distributed training (Hadoop, Spark MLlib) -Parallelization strategies -GPU/TPU acceleration Interpretability and Explainability: -SHAP, LIME, feature importance -Explainable AI in engineering applications -Ethical considerations ML for Engineering Applications: -Predictive maintenance -Computer vision for defect detection -Control systems and optimization Future Trends in Machine Learning: -Federated learning -Self-supervised learning -AI safety and ethical AI engineering Who is it for? - Engineering students preparing for exam. - Professionals brushing up on their knowledge. - Anyone interested in understanding Machine Learning concepts. Download now and make learning Machine Learning enjoyable and effective! Terms of Use: https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ Privacy Policy: https://forwaelade.web.app/MachineLearning/privacy-policy