Introduction to Machine learning
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The document provides an overview of machine learning, describing it as an AI technique that enables systems to learn from historical data without explicit programming. It discusses various applications, programming languages, learning types, and frameworks used in the field, as well as the processes involved in training machine learning models. Key technologies such as GPUs and TensorFlow for computational tasks are also highlighted.
Introduction to Machine learning
- 2. Overview • An Artificial Intelligence (AI) technique which provides the system to learn by itself. • Machine acts without being explicitly programmed • Utilizes the historical data to make better business decisions • Evolved from pattern recognition and computation theory of Artificial Intelligence • Basically an algorithm not a magic
- 3. Application Areas • Self driving cars • Practical speech recognition • Efficient web search • Human genome understanding • Object detection • Face detection and recognition • Vehicle monitoring for CCTV • Email spam detection / Cyber fraud detection • Online recommendation
- 4. When to use • Cannot code the rules – Scenario which cannot be solved using a deterministic rule based solution – Rule depends on too many factors – Many rules overlap and needs to be fine tuned – Difficult scenario for a human to code the rules • Cannot scale – Effective to handle large scale problems
- 5. Programming Language • MATLAB – Excellent tool for representation and working with matrices • R – Platform used to understand and explore the data using statistical methods and graphs • Python – Popular scientific language and rising star of ML. • JAVA/C
- 6. ML Types Labeled Data Labeled Data Unlabeled Data Unlabeled Data Model Model Model
- 7. Different Types • Supervised Learning – Analyses the training data and produces output accordingly – Algorithm iteratively makes predictions on the training data – Neural Networks, Multi Layer Perception, Decision Trees • Un supervised Learning – Learn to inherent structure from input data – Clustering, Distances and Normalization, Self Organizing maps. • Semi – Supervised – Mixture of supervised and un-supervised techniques
- 9. Process Involved • Data Collection • Data Preparation • Model Selection • Training • Evaluation • Prediction
- 10. Training Process • Input training data source • Name of the attribute that contains target to be predicted • Required data transformation instructions • Parameters to control the learning algorithm
- 11. Model • Refers to the model artifact created by the training process • Provides the ML algorithm with training data to learn from. • Model Zoo – Created with multiple datasets like COCO, Kitti and OpenImages
- 12. Models • Binary Classification Model – Predicts a binary outcome ( one of two possible classes ) • Multi class Classification Model – Generates predictions for multiple classes • Regression Model – Predicts a numeric value – How many units will sell tomorrow
- 13. Dataset • Training set – Set of examples used for learning with known target • Validation set – Set of examples used to fine tune the classifier and estimate the error • Test set – Used to access the performance of the classifier
- 14. Machine Learning Frameworks • H2o.ai • Apache Singa • Amazon Machine Learning • Azure ML Studio • Massive Online Analysis • Mlpack • Spark Mlib • Tensorflow • Caffe2
- 15. ML Examples • Object Detection & Recognition • Multi Vehicle / Car Detection • Vehicle Speed detection
- 16. OpenCV • Open Source Computer Vision Library • Library functions mainly aimed at real-time computer vision, image processing and machine learning • Has C++, JAVA, Python library interface • Now features GPU Acceleration for real time operations
- 17. GPU • Graphic Processing Unit • Used to render 3D graphics comprised on polygons • Technologies like OpenCV, OpenCL, CUDA used to assist the GPU in non-graphics computations • Improves the overall performance of the computer • Used to accelerate the deep learning, analytics and engineering applications.
- 18. CUDA • Parallel computing platform and programming model developed by NVIDIA • Able to speed up the computing applications by harnessing the power of GPUs • GPU accelerated computing – Sequential part of workload runs on CPU – Intensive portion of application runs on thousands of GPU cores in parallel
- 19. Tensorflow • Open source machine learning framework for everyone • Numerical computation using data flow graphics • Deploy computation on one or more CPUs or GPUs in desktop • Developed by Google • Also supports hardware acceleration with Android Neural Networks APIs.
- 20. Combinations • Tensorflow 1.4 – Nvidia CUDA 8.0 • Tensorflow 1.5 – Nvidia CUDA 9.0
- 21. References • https://docs.aws.amazon.com/machine- learning/latest/dg/ • https://developers.google.com/machine- learning/crash-course/ • https://en.wikipedia.org/wiki/Machine_learni ng • https://www.coursera.org/learn/machine- learning
- 22. Thank You