Machine Learning

   Deep Learning UNIT   I Deep Learning:   Fundamentals Introduction Building Block of Neural Networks Layers MLPs Forward   pass backward   pass class trainer   and   optimizer The   Vanishing   and   Exploding   Gradient   Problems Difficulties in Convergence Local and Spurious Optima Preprocessing Momentum learning rate Decay Weight Initialization Regularization Dropout SoftMax Cross Entropy loss   function Activation   Functions 👉 Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes 👉 Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs 👉 Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes 👉 Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs 👉 Deep Learning: UNIT 1: Deep Learning Fundamentals -Long Answer Questions 👉 Deep Learning: UNIT 1: Deep Learning Fundamentals - Short Answer Questions

                                                                                                 UNIT-1 B Deep Learning: Fundamentals 1.      The Softmax Function 2.      Cross-Entropy Loss Function 3.      Activation Functions 4.      Preprocessing 5.      Momentum 6.      Learning Rate Decay 7.      Weight Initialization 8.      Regularization 9.      Dropout

 Deep Learning UNIT I Deep Learning: Fundamentals, Introduction, Building Block of Neural Networks, Layers, MLPs, Forward pass, backward pass, class, trainer and optimizer, The Vanishing and Exploding Gradient Problems, Difficulties in Convergence, Local and Spurious Optima, Preprocessing, Momentum, learning rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross Entropy loss function, Activation Functions. 👉 Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes 👉 Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs 👉 Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes 👉 Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs 👉 Deep Learning: UNIT 1: Deep Learning - Fundamentals: Long Answer Questions 👉 Deep Learning: UNIT 1: Deep Learning - Fundamentals : Short Answer Questions   UNIT II CNN: Introduction, striding and padding, pooling layers , structure, o

UNIT – II (B) Artificial Neural Networks Introduction Neural Network Representation Appropriate Problems for Neural Network Learning Perceptron’s   Multilayer Networks and the Back-Propagation Algorithm.

UNIT-2 (A)  Decision Tree Learning   Decision Tree Learning  : Introduction Decision Tree Representation Appropriate Problems for Decision Tree Learning The Basic Decision Tree Learning Algorithm Issues In Decision Tree Learning  

UNIT-2 (A) Decision Tree Learning   Decision Tree Learning: Introduction Decision Tree Representation Appropriate Problems for Decision Tree Learning The Basic Decision Tree Learning Algorithm Issues In Decision Tree Learning  

Machine Learning  Introduction Concept Learning and the General to Specific Ordering Decision Tree Learning Artificial Neural Networks Bayesian Learning Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical Learning Reinforcement Learning 👉 Machine Learning 2 Syllabus UNIT-1 :  Introduction &  Concept Learning and the General to Specific Ordering Introduction - Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning. Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm. 👉 Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes 👉 Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs 👉 Machine Learning 2- UNI

  Machine Learning 👉    About Machine Learning 1 The Machine Learning Landscape Classification Support Vector Machines Decision Trees Ensemble Learning and Random Forests Dimensionality Reduction Clustering 👉   About Machine Learning 2   Introduction Concept Learning and the General to Specific Ordering Decision   Tree   Learning Artificial Neural Networks Bayesian Learning Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical   Learning Reinforcement Learning 👉  About Machine Learning 3 Introduction  Data Pre-processing Performance measurement of models  Supervised Learning  Decision Tree Learning  Unsupervised Learning  Ensemble Models

Deep Learning   👉 Deep Learning Syllabus 👉 Deep Learning: Fundamentals 👉 CNN 👉 RNN 👉Autoencoders 👉Transfer   Learning UNIT I Deep Learning: Fundamentals, Introduction, Building Block of Neural Networks, Layers, MLPs, Forward pass, backward pass, class, trainer and optimizer, The Vanishing and Exploding Gradient Problems, Difficulties in Convergence, Local and Spurious Optima, Preprocessing, Momentum, learning rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross Entropy loss function, Activation Functions. UNIT II CNN: Introduction, striding and padding, pooling layers, structure, operations and prediction of CNN with layers, CNN -Case study with MNIST, CNN VS Fully Connected UNIT III RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM UNIT IV Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational Autoencoders, GANS UNIT V Transfer Learning - Types, Methodologies, Diving into Transfer Learn