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Deep Learning Bootcamp Neural Networks With Python, Pytorch

سرفصل های دوره

1 - Deep LearningDeep Neural Network for Beginners Using Python
  • 1 - Promo Highlights
  • 2 - Introduction Introduction to Instructor and Aisciences
  • 3 - Links for the Courses Materials and Codes.html
  • 4 - Basics of Deep Learning Problem to Solve Part 1
  • 5 - Basics of Deep Learning Problem to Solve Part 2
  • 6 - Basics of Deep Learning Problem to Solve Part 3
  • 7 - Basics of Deep Learning Linear Equation
  • 8 - Basics of Deep Learning Linear Equation Vectorized
  • 9 - Basics of Deep Learning 3D Feature Space
  • 10 - Basics of Deep Learning N Dimensional Space
  • 11 - Basics of Deep Learning Theory of Perceptron
  • 12 - Basics of Deep Learning Implementing Basic Perceptron
  • 13 - Basics of Deep Learning Logical Gates for Perceptrons
  • 14 - Basics of Deep Learning Perceptron Training Part 1
  • 15 - Basics of Deep Learning Perceptron Training Part 2
  • 16 - Basics of Deep Learning Learning Rate
  • 17 - Basics of Deep Learning Perceptron Training Part 3
  • 18 - Basics of Deep Learning Perceptron Algorithm
  • 19 - Basics of Deep Learning Coading Perceptron Algo Data Reading Visualization
  • 20 - Basics of Deep Learning Coading Perceptron Algo Perceptron Step
  • 21 - Basics of Deep Learning Coading Perceptron Algo Training Perceptron
  • 22 - Basics of Deep Learning Coading Perceptron Algo Visualizing the Results
  • 23 - Basics of Deep Learning Problem with Linear Solutions
  • 24 - Basics of Deep Learning Solution to Problem
  • 25 - Basics of Deep Learning Error Functions
  • 26 - Basics of Deep Learning Discrete vs Continuous Error Function
  • 27 - Basics of Deep Learning Sigmoid Function
  • 28 - Basics of Deep Learning MultiClass Problem
  • 29 - Basics of Deep Learning Problem of Negative Scores
  • 30 - Basics of Deep Learning Need of Softmax
  • 31 - Basics of Deep Learning Coding Softmax
  • 32 - Basics of Deep Learning One Hot Encoding
  • 33 - Basics of Deep Learning Maximum Likelihood Part 1
  • 34 - Basics of Deep Learning Maximum Likelihood Part 2
  • 35 - Basics of Deep Learning Cross Entropy
  • 36 - Basics of Deep Learning Cross Entropy Formulation
  • 37 - Basics of Deep Learning Multi Class Cross Entropy
  • 38 - Basics of Deep Learning Cross Entropy Implementation
  • 39 - Basics of Deep Learning Sigmoid Function Implementation
  • 40 - Basics of Deep Learning Output Function Implementation
  • 41 - Deep Learning Introduction to Gradient Decent
  • 42 - Deep Learning Convex Functions
  • 43 - Deep Learning Use of Derivatives
  • 44 - Deep Learning How Gradient Decent Works
  • 45 - Deep Learning Gradient Step
  • 46 - Deep Learning Logistic Regression Algorithm
  • 47 - Deep Learning Data Visualization and Reading
  • 48 - Deep Learning Updating Weights in Python
  • 49 - Deep Learning Implementing Logistic Regression
  • 50 - Deep Learning Visualization and Results
  • 51 - Deep Learning Gradient Decent vs Perceptron
  • 52 - Deep Learning Linear to Non Linear Boundaries
  • 53 - Deep Learning Combining Probabilities
  • 54 - Deep Learning Weighted Sums
  • 55 - Deep Learning Neural Network Architecture
  • 56 - Deep Learning Layers and DEEP Networks
  • 57 - Deep Learning Multi Class Classification
  • 58 - Deep Learning Basics of Feed Forward
  • 59 - Deep Learning Feed Forward for DEEP Net
  • 60 - Deep Learning Deep Learning Algo Overview
  • 61 - Deep Learning Basics of Back Propagation
  • 62 - Deep Learning Updating Weights
  • 63 - Deep Learning Chain Rule for BackPropagation
  • 64 - Deep Learning Sigma Prime
  • 65 - Deep Learning Data Analysis NN Implementation
  • 66 - Deep Learning One Hot Encoding NN Implementation
  • 67 - Deep Learning Scaling the Data NN Implementation
  • 68 - Deep Learning Splitting the Data NN Implementation
  • 69 - Deep Learning Helper Functions NN Implementation
  • 70 - Deep Learning Training NN Implementation
  • 71 - Deep Learning Testing NN Implementation
  • 72 - Optimizations Underfitting vs Overfitting
  • 73 - Optimizations Early Stopping
  • 74 - Optimizations Quiz
  • 75 - Optimizations Solution Regularization
  • 76 - Optimizations L1 L2 Regularization
  • 77 - Optimizations Dropout
  • 78 - Optimizations Local Minima Problem
  • 79 - Optimizations Random Restart Solution
  • 80 - Optimizations Vanishing Gradient Problem
  • 81 - Optimizations Other Activation Functions
  • 82 - Final Project Final Project Part 1
  • 83 - Final Project Final Project Part 2
  • 84 - Final Project Final Project Part 3
  • 85 - Final Project Final Project Part 4
  • 86 - Final Project Final Project Part 5

  • 2 - PyTorch Power From Zero to Deep Learning Hero PyTorch
  • 87 - Links for the Courses Materials and Codes.html
  • 88 - Introduction Module Content
  • 89 - Introduction Benefits of Framework
  • 90 - Introduction Installations and Setups
  • 91 - Tensor Introduction to Tensor
  • 92 - Tensor List vs Array vs Tensor
  • 93 - Tensor Arithmetic Operations
  • 94 - Tensor Tensor Operations
  • 95 - Tensor AutoGradiants
  • 96 - Tensor Activity Solution
  • 97 - Tensor Detaching Gradients
  • 98 - Tensor Loading GPU
  • 99 - NN with Tensor Introduction to Module
  • 100 - NN with Tensor Basic NN part 1
  • 101 - NN with Tensor Basic NN part 2
  • 102 - Loss-Functions.pdf
  • 102 - NN with Tensor Loss Functions
  • 103 - Activation-Function.pdf
  • 103 - NN with Tensor Activation Functions Hidden Layers
  • 104 - NN with Tensor Optimizers
  • 104 - Optimizer.pdf
  • 105 - NN with Tensor Data Loader Dataset
  • 106 - NN with Tensor Activity
  • 107 - NN with Tensor Activity Solution
  • 108 - NN with Tensor Formating the Output
  • 109 - NN with Tensor Graph for Loss
  • 110 - CNN Introduction to Module
  • 111 - CNN CNN vs NN
  • 112 - CNN Introduction to Convolution
  • 113 - CNN Convolution Animations
  • 114 - CNN Convolution using Pytorch
  • 115 - CNN Introduction to Pooling
  • 116 - CNN Pooling using Numpy
  • 117 - CNN Pooling in Pytorch
  • 118 - CNN Introduction to Project
  • 119 - CNN Project Data Loading
  • 120 - CNN Project Transforms
  • 121 - CNN Project DataLoaders
  • 122 - CNN Project CNN Architect
  • 123 - CNN Project Forward Propagation
  • 124 - CNN Project Training CNN
  • 125 - CNN Project Analyzing Model Output
  • 126 - CNN Project Making Predictions

  • 3 - TensorFlow Fundamentals From Basics to Brilliant AI Project
  • 127 - Links for the Courses Materials and Codes.html
  • 128 - Introduction to TensorFlow Module Introduction
  • 129 - Introduction to TensorFlow TensorFlow Definition and Properties
  • 130 - Introduction to TensorFlow Tensor Types and Tesnor Board
  • 131 - Introduction to TensorFlow How to use TensorFlow
  • 132 - Introduction to TensorFlow Google Colab
  • 133 - Introduction to TensorFlow Exercise
  • 134 - Introduction to TensorFlow Exercise Solution
  • 135 - Introduction to TensorFlow Quiz
  • 136 - Introduction to TensorFlow Quiz Solution
  • 137 - Building your first deep learning Project Module Introduction
  • 138 - Building your first deep learning Project ANNs
  • 139 - Building your first deep learning Project TensorFlow Playground
  • 140 - Building your first deep learning Project Load TF and Data
  • 141 - Building your first deep learning Project Model Training and Evaluation
  • 142 - Building your first deep learning Project Project
  • 143 - Building your first deep learning Project Project Implementation
  • 144 - Building your first deep learning Project Quiz
  • 145 - Building your first deep learning Project Quiz Solution
  • 146 - Multilayer Deep Learning Project Module Introduction
  • 147 - Multilayer Deep Learning Project Training and Epochs
  • 148 - Multilayer Deep Learning Project Gradient Decent and Back Propagation
  • 149 - Multilayer Deep Learning Project Bias Variance TradeOff
  • 150 - Multilayer Deep Learning Project Performance Metrics
  • 151 - Multilayer Deep Learning Project ProjectSales Predition
  • 152 - Multilayer Deep Learning Project Quiz
  • 153 - Multilayer Deep Learning Project Quiz Solution
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    تاریخ انتشار: ۲۵ اردیبهشت ۱۴۰۴
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