Machine Learning

UNIT V  Transfer Learning  Types  Methodologies  Diving into Transfer Learning  Challenges  

  UNIT V  Transfer Learning  1. Types  2. Methodologies  3. Diving into Transfer Learning  4. Challenges 👉 Transfer learning Notes 👉 Transfer Learning PPTs

UNIT V  Transfer Learning  1. Types  2. Methodologies  3. Diving into Transfer Learning  4. Challenges   Reference: Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”, Manning Publications, 2021

 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 , stru...

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 int...