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PPT2: Introduction of Machine Learning & Deep Learning and its types
- 5. What is ML? “ Learning is any process by which a system improves performance from experience.” Herbert Alexander Simon Machine Learning is a way to do programming in computers to optimize a performance criterion using data or past experience.
- 6. Why Machine Learning? ● Ability to mimic human and replace certain monotonous tasks which require some intelligence. like recognizing handwritten characters ● Discover new knowledge from large databases (data mining). Market basket analysis ● Develop systems that can automatically adapt and customize themselves to individual users. Personalized news or mail folder ● Develop systems that are too difficult/expensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task.
- 7. Machine Learning Types ● Supervised Learning ● Unsupervised Learning ● SemiSupervised Learning ● Reinforcement Learning
- 8. Supervised Learning Raw Data Sample Data, code and lost new sample data – feedback (Model – Train) Algorithm Product of trained algorithm Manual Verification Production Supervised Learning ➔ Makes machine learn explicitly ➔ Data with clearly defined output is given ➔ Direct feedback is given ➔ Predicts outcome/future ➔ Resolve classification and regression problems
- 9. Unsupervised Learning Raw Data Algorithm Automated Clusters Manual Review Production Unsupervised Learning ➔ Machine understands the data (identifies patterns/ structures) ➔ Evaluation is qualitative or indirect ➔ Does not predict/find anything specific
- 11. Reinforcement Learning Raw Data Sample Data, Code and lost new sample data – feedback (Model Train) Code and test new sample – Feedback (Model – Train) Algorithm Product of trained algorithm Reinforcement Learning ➔ An approach to AI ➔ Reward based learning ➔ Learning form +ve & +ve reinforcement ➔ Machine Learns how to act in a certain environment ➔ To maximize rewards
- 12. Machine Learning ● Linear SVD ● Polynomial K-Means ● Logistic PCA( Principal Component Analysis) ● KNN Apriori ● Trees FP - Growth ● Naive-Bayes ● SVM (Support Vector Machine) Supervised Unsupervised Reinforcement Regression Association analysisClassification Clustering Hidden Markov Model Continuous Categorical
- 13. Steps of ML ● Preprocessing ● Feature Engineering { Data visualization} ● Model Selection ● Model Building ● Model Training ● Testing ● Validation
- 14. Process Flow Raw Survey Data Preprocessing Criteria Labeling Outlier Detection Random Forests Classification Algorithms Grid Search Ad Clicks Raw database Final personaModel optimization A/B testing Feature selection Supervised learning Unsupervised learning Labeled dataset Cleaned dataset
- 15. Steps of ML ● Preprocessing ● Feature Engineering { Data visualization} ● Model Selection ● Model Building ● Model Training ● Testing ● Validation
- 18. DEEP LEARNING
- 19. What is DL? Deep Learning is a branch of ML which is completely based on ANN (Artificial Neural Networks), a concept inspired by the biological neural network, as neural network is going to mimic the human brain . Deep learning algorithms attempt to learn representation by using a hierarchy of multiple layers. If we provide the system tons of information, it begins to understand it and respond in useful ways.
- 21. Let’s look at ANN in DL: It is a concept inspired by the biological neural network. It consists of three layers: These layers consist of nodes that are interconnected with each other. INPUT LAYER Used for taking input data from external sources and passing to hidden layers. Does not perform any computation. HIDDEN LAYER Consists of many hidden layers. All computation is performed here. Passes the output to the output layer. OUTPUT LAYER Used for computing Giving the output to the outside world
- 22. ANN Flowchart: Inputs Weights Summation and Bias Activation Output 1. Inputs are fed into the perceptron (the basic computational element (neuron) is called a node (or unit)). 2. Weights are multiplied to each input 3. Summation and then add bias 4. Activation function is applied. 5. Output is either triggered as 1, or not, as 0.
- 23. ANN to DNN ANNs or general NNs consist of Multilayer Perceptron’s (MLP) which contain one or more hidden layers with multiple hidden units (neurons) in them. The gradient descent approach is a firstorder optimization algorithm which is used for finding the local minima of an objective function. A long training time is the main drawback for the traditional gradient descent approach, the SGD (Stochastic Gradient Descent) approach is used for training Deep Neural Networks (DNN).
- 24. Neural Networks In DL, there are fundamental architectures of neural network that perform on different types of data which are: ● Deep Neural Networks (DNNs) are typically Feed Forward Neural Networks (FFNNs) ✔ Convolutional Neural Network (CNN) ✔ Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) a special kind of RNN
- 25. DNN DNNs are typically FFNNs in which data flows from the input layer to the output layer without going backward and they never touch a node again. DNN forgets the input topology while CNN does not.
- 26. Difference Between CNN & RNN CNN ● CNN were designed to map image data to an output variable,i.e; ability to develop an internal representation of a two dimensional image. ● It considers only the current input. ● Four layers namely: Convolution layer, ReLU layer, Pooling, Fully Connected Layer. ● Use CNNs For: Image data Classification prediction problems Regression prediction problems RNN ● It works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. ● It considers the current input and previously received inputs. ● Four types namely: One to One, One to Many, Many to One, Many to Many. ● Use RNNs For: Text data Speech data Generative model Classification prediction problems Regression prediction problems
- 27. CNN and RNN looks like: CNN Looks Like: RNN Looks Like:
- 29. Why DL is Useful? ● DL provides a very flexible, universal, learnable framework for representing world, visual and linguistic information. ● Learned features are easy to adopt, fast to learn ● Manually designed features are often overspecified, incomplete and take a long time ● Can learn both unsupervised & supervised ● Effective endto end joint system learning ● Utilize large amounts of training data
- 30. Application of Deep Learning INTERNET & CLOUD ✔ Image Classification ✔ Speech Recognition ✔ Language Translation ✔ Language Processing ✔ Sentiment Analysis ✔ Recommendation AUTONOMOUS MACHONES ✔ Pedestrian Detection ✔ Lane Tracking ✔ Recognize Traffic Sign SECURITY & DEFENCE ✔ Face Detection ✔ Video Surveillance ✔ Satellite Imagery MEDICINE & BIOLOGY ✔ Cancer Cell Detection ✔ Diabetic Grading ✔ Drug Discovery MEDIA & ENTERTAINMENT ✔ Video Captioning ✔ Real Time Translation ✔ Video Search
- 32. ML vs DL Machine Learning ● Thousands of data points. ● Numerical value, like a classification or score. ● Uses various types of automated algorithms that learn to model functions and predict future actions from data. ● Algorithms are directed by data analysis to examine specific variables in data sets. Deep Learning ● Big data: million of data points ● Anything from numerical values to freeform elements, like free text and sound ● Uses neural networks that pass data through many processing layers to interpret data features and relationships ● Algorithms are largely self directed on data analysis once they’re put into production Optimal data volumes How it’s managed How it works Outputs
- 34. Conclusion: We have seen the difference between AI, ML, DL. Detail study of ML: introduction of ML, why we are using it, types of ML, steps of ML, flow of working process. We have also covered up DL, why we are using it, working of DL. In last we differentiate between ML and DL.