Machine learning presentation
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Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
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Machine learning seminar presentation
This document presents an overview of machine learning. It defines machine learning as a field that allows computers to learn without being explicitly programmed, and discusses how machine learning enables computers to automatically analyze large datasets to make predictions. The document then summarizes different types of machine learning techniques including supervised learning, unsupervised learning, reinforcement learning, and more. It provides examples of applications of machine learning like face recognition, speech recognition, and self-driving cars. In conclusion, it states that machine learning is already used across many industries and can improve lives in numerous ways.
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Machine learning presentation
- 1. Machine Learning The Era of - Saif Arsalan Saurav Prasad
- 2. Machine Learning and AI
- 3. What is machine learning? "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." - Tom M. Mitchell (Professor at Carnegie Mellon University) Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
- 4. The Evolution - Experiences Data Problem/Task Bias Performance The term “Machine Learning” was first coined by Arthur Samuels. •1950s:–Samuel's checker-playing program •1960s:–Neural network: Rosenblatt's perceptron •1970s:–Natural language processing. •1980s:–Advanced decision tree and rule learning , Resurgence of neural network 1994 - Google’s Self Driving car 1997 - Deep Blue beats Gary Kasparov LEARNER MODEL
- 5. Why a sudden boom in AI and Machine Learning? ● High performance machine ● Lots and lots of data
- 7. Google's deep learning AI system can detect cancer faster, and with higher accuracy than human professionals.
- 8. TEXT MINING BUSINESS INTELLIGENCE/RISK MANAGEMENT ORACLE , MICROSOFT AND SAP HAVE 98% ACCURATE INTEGRATED BUSINESS AI ALGORITHMS WHICH AWARES THEM FROM HIGH RISKS AND PROVIDES STATISTICAL MODELS.
- 10. SUPERVISED LEARNING X Y Input 1 Output 1 Input 2 Output 2 Input 3 Output 3 Input N Output N
- 11. CLASSIFICATION Example: Credit scoring Differentiating between low- risk and high-risk customers from their income and savings
- 12. REGRESSION Example : Predict the price of used car. X : car attributes(mileage) Y: price of the car.
- 14. X Input 1 Input 2 Input 3 Input N UNSUPERVISED LEARNING
- 15. This Black box contains Millions of Random Images with ground truths. Classifier 1 Classifier 2 Classifier 3 Step 1 : Clustering - Unsupervised Learning Step 2 : Supervised Learning
- 17. Deep Learning Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification.
- 18. ● Each node in the hidden and output layers has a classifier. ● The input neurons receive the data features. After processing the data, they send output to the first hidden layer. ● The hidden layer processes this output and sends results to the next hidden layer. ● The data reaches the final output layer, where the output value determines the object's classification. ● Process is known as Forward Propagation, or Forward prop.
- 19. Weights and Biases Before a neuron fires its output to the next neuron in the network, it must first process the input. To do so, it performs a basic calculation with the input and two other numbers, referred to as the weight and the bias. 0.1 0.2 0.3 0.4 0.5 Value is processed using some mathematical operations
- 20. 28x28 pixels 784 pixels with some value(0-1) on basis of greyscale
- 21. 784 16 16 10 Value of neuron in next layer X = sigmoid(w(0,0)*v(0,0)+w(0,1)*v(0,1)+ …...w(0,784)*v(0,784) + b(0)) Sigmoid function w = weight b = bias 784x16 + 16x16 + 16x10 weights 16 + 16 + 10 Biases 13,002
- 22. SAURAV PRASAD SAIF ARSALAN sauravprasad.1996@gmail.com saifsocial2711@gmail.com THANK YOU !