Deep Learning : Image Classification with Tensorflow in 2023

سرفصل های دوره

Master and Deploy Image Classification solutions with Tensorflow using models like Convnets and Vision Transformers

1 - Introduction

1 - Welcome

2 - General Introduction

2 - Tensors and variables

3 - Basics

4 - Initialization and Casting

5 - Indexing

6 - Maths Operations

7 - Linear Algebra Operations

8 - Common Methods

9 - RaggedTensors

10 - Sparse Tensors

11 - String Tensors

12 - Variables

3 - PREREQUISCITE Building neural networks with Tensorflow

13 - Understanding the Task

14 - Data Preparation

15 - Linear Regression Model

16 - Error Sanctioning

17 - Training and Optimization

18 - Performance Measurement

19 - Validation and Testing

20 - Corrective Measures

4 - Building convnets with tensorflow

21 - Understanding the Task

22 - Data Preparation

23 - Data Visualization

24 - Data Processing

25 - How and Why Convolutional Neural Networks Work

26 - Building ConvNets with TensorFlow

27 - Binary Crossentropy Loss

28 - Training

29 - Model Evaluation and Testing

30 - Loading and Saving tensorflow models to gdrive

5 - Building more advanced TensorFlow Models with Functional API Subclassing and Cu

31 - Functional API

32 - Model Subclassing

33 - Custom Layers

6 - Evaluating Classification Models

34 - PrecisionRecallAccuracy

35 - Confusion Matrix

36 - ROC curve

7 - Improving Model Performance

37 - Callbacks with TensorFlow

38 - Learning Rate Scheduling

39 - Model Checkpointing

40 - Mitigating Overfitting and Underfitting with Dropout Regularization

8 - Data Augmentation

41 - Data augmentation with TensorFlow using tfimage and Keras Layers

42 - Mixup Data augmentation with TensorFlow 2 with intergration in tfdata

43 - Cutmix Data augmentation with TensorFlow 2 and intergration in tfdata

44 - Albumentations with TensorFlow 2 and PyTorch for Data augmentation

9 - Advanced Tensorflow

45 - Custom Loss and Metrics in TensorFlow 2

46 - Eager and Graph Modes in TensorFlow 2

47 - Custom Training Loops in TensorFlow 2

10 - Tensorboard integration with TensorFlow 2

48 - Log data

49 - view model graphs

50 - hyperparameter tuning

51 - Profiling and other visualizations with Tensorboard

11 - MLOps with Weights and Biases

52 - Experiment Tracking

53 - Hyperparameter Tuning with Weights and Biases and TensorFlow 2

54 - Dataset Versioning with Weights and Biases and TensorFlow 2

55 - Model Versioning with Weights and Biases and TensorFlow 2

12 - Human Emotions Detection

56 - data preparation

57 - Modeling and Training

58 - Data augmentation

59 - Tensorflow records

13 - Modern Convolutional Neural Networks

60 - Alexnet

61 - vggnet

62 - resnet

63 - coding resnet

64 - mobilenet

65 - efficientnet

14 - Transfer learning

66 - Pretrained Models

67 - Finetuning

15 - Understanding the blackbox

68 - visualizing intermediate layers

69 - gradcam method

16 - Class Imbalance and Ensembling

70 - Ensembling

71 - Class imbalance

17 - Transformers in Vision

72 - Understanding VITs

73 - Building VITs from scratch

74 - Finetuning Huggingface VITs

75 - Model Evaluation with Wandb

76 - Data efficient Transformers

77 - Swin Transformers

18 - Deploying the Image classification model

78 - Conversion from tensorflow to Onnx Model

79 - Understanding quantization

80 - Practical quantization of Onnx Model

81 - Quantization Aware training

82 - Conversion to tensorflowlite model

83 - How APIs work

84 - Building API with Fastapi

85 - Deploying API to the Cloud

86 - Load testing API

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تولید کننده: Udemy-Training