19:44
Deep Learning Fundamentals with PyTorch Series | Part One | Intro to PyTorch
Visual Learners with Teddy Tassew
21:15
Deep Learning Fundamentals with PyTorch Series | Part Two | Course Overview & Installation
11:46
PyTorch Basics | Part One | Scalars, Arrays, and Matrix
19:13
PyTorch Basics | Part Two | Rank, Axes, and Shape
39:05
PyTorch Basics | Part Three | Creating PyTorch Tensors
35:39
PyTorch Basics | Part Four | Tensor Operations | Reshaping Operations
39:45
PyTorch Basics | Part Five | Tensor Operations | Reshaping Continued (Stacking and Flatenning)
37:21
PyTorch Basics | Part Six | Tensor Operations | Broadcasting and Elementwise Operations
39:24
PyTorch Basics | Part Seven | Tensor Operations | ArgMax and Reduction Operations
52:38
PyTorch Basics | Part Eight | Gradients Theory | Computation graph, Autograd, and Back Propagation
43:13
PyTorch Basics | Part Nine | Gradients Implementation | Autograd and Back Propagation
1:01:51
PyTorch Basics | Part Ten | Loss Theory
55:37
PyTorch Basics | Part Eleven | Loss Implementation
59:30
PyTorch Basics | Optimizers Theory | Part One | Gradient Descent
44:02
PyTorch Basics | Optimizers Theory | Part Two | Gradient Descent with Momentum, RMSProp, Adam
8:56
PyTorch Basics | Break Session | Why learn PyTorch? | Lex Fridman interview with Jeremy Howard
54:56
PyTorch Basics | Part Fifteen | Optimizers Implementation
33:27
PyTorch Basics | Part Sixteen | Linear Regression
36:57
PyTorch Basics | Part Seventeen | Linear Regression Implementation
25:14
PyTorch Basics | Part Eighteen | Logistic Regression
1:00:03
PyTorch Basics | Part Nineteen | Logistic Regression Implementation
