Machine Learning

  UNIT III RNN ------------------------------------------------------------------------------------------------------------------------ 1.      Handling Branches 2.      Layers 3.      Nodes 4.      Essential Elements 5.      Vanilla RNNs 6.      GRUs 7.      LSTM   👉  Deep Learning: UNIT 3 (A): RNN: Notes 👉 Deep Learning: UNIT 3 (A) : RNN : PPTs 👉  Deep Learning: UNIT- 3: RNN: Long Answer Questions 👉  Deep Learning: UNIT 3 : RNN : Short Answer Questions

  UNIT III (A) RNN -------------------------------------------------------------------------------------------------------------------- 1.      Handling Branches 2.      Layers 3.      Nodes 4.      Essential Elements

UNIT III (A) RNN 1.      Handling Branches 2.      Layers 3.      Nodes 4.      Essential Elements

   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 I (A) Deep Learning: Fundamentals 1.       Introduction 2.       Building Block of Neural Networks 3.       Layers 4.       MLPs 5.       Forward pass 6.       backward pass 7.       class 8.       trainer and optimizer 9.       The Vanishing and Exploding Gradient Problems 10.   Difficulties in Convergence 11.   Local and Spurious Optima

                                                                                                 UNIT I (A) Deep Learning: Fundamentals 1.       Introduction 2.       Building Block of Neural Networks 3.       Layers 4.       MLPs 5.       Forward pass 6.       backward pass 7.       class 8.       trainer and optimizer 9.       The Vanishing and Exploding Gradient Problems 10.   Difficulties in Convergence 11.   Local and Spurious Optima