Deep Learning : Image Classification with Tensorflow in 2023

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Neuralearn Dot AI

32:28:03

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1 - Introduction

1 - Welcome.mp4

03:39

2 - General Introduction.mp4

30:09

2 - Tensors and variables

3 - Basics.mp4

08:06

4 - Initialization and Casting.mp4

01:03:40

5 - Indexing.mp4

14:28

6 - Maths Operations.mp4

32:49

7 - Linear Algebra Operations.mp4

01:01:16

8 - Common Methods.mp4

53:50

9 - RaggedTensors.mp4

11:20

10 - Sparse Tensors.mp4

02:41

11 - String Tensors.mp4

03:21

12 - Variables.mp4

07:05

3 - PREREQUISCITE Building neural networks with Tensorflow

13 - Understanding the Task.mp4

04:51

14 - Data Preparation.mp4

34:56

15 - Linear Regression Model.mp4

15:30

16 - Error Sanctioning.mp4

14:32

17 - Training and Optimization.mp4

16:27

18 - Performance Measurement.mp4

02:53

19 - Validation and Testing.mp4

20:09

20 - Corrective Measures.mp4

24:19

4 - Building convnets with tensorflow

21 - Understanding the Task.mp4

08:45

22 - Data Preparation.mp4

19:57

23 - Data Visualization.mp4

02:35

24 - Data Processing.mp4

08:29

25 - How and Why Convolutional Neural Networks Work.mp4

47:20

26 - Building ConvNets with TensorFlow.mp4

06:19

27 - Binary Crossentropy Loss.mp4

07:33

28 - Training.mp4

13:16

29 - Model Evaluation and Testing.mp4

05:40

30 - Loading and Saving tensorflow models to gdrive.mp4

17:52

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

31 - Functional API.mp4

16:32

32 - Model Subclassing.mp4

15:16

33 - Custom Layers.mp4

17:36

6 - Evaluating Classification Models

34 - PrecisionRecallAccuracy.mp4

23:46

35 - Confusion Matrix.mp4

09:29

36 - ROC curve.mp4

07:57

7 - Improving Model Performance

37 - Callbacks with TensorFlow.mp4

25:41

38 - Learning Rate Scheduling.mp4

17:24

39 - Model Checkpointing.mp4

07:55

40 - Mitigating Overfitting and Underfitting with Dropout Regularization.mp4

29:22

8 - Data Augmentation

41 - Data augmentation with TensorFlow using tfimage and Keras Layers.mp4

53:19

42 - Mixup Data augmentation with TensorFlow 2 with intergration in tfdata.mp4

18:33

43 - Cutmix Data augmentation with TensorFlow 2 and intergration in tfdata.mp4

41:49

44 - Albumentations with TensorFlow 2 and PyTorch for Data augmentation.mp4

01:14:12

9 - Advanced Tensorflow

45 - Custom Loss and Metrics in TensorFlow 2.mp4

19:51

46 - Eager and Graph Modes in TensorFlow 2.mp4

12:49

47 - Custom Training Loops in TensorFlow 2.mp4

25:33

10 - Tensorboard integration with TensorFlow 2

48 - Log data.mp4

31:54

49 - view model graphs.mp4

02:46

50 - hyperparameter tuning.mp4

20:49

51 - Profiling and other visualizations with Tensorboard.mp4

07:53

11 - MLOps with Weights and Biases

52 - Experiment Tracking.mp4

54:21

53 - Hyperparameter Tuning with Weights and Biases and TensorFlow 2.mp4

26:24

54 - Dataset Versioning with Weights and Biases and TensorFlow 2.mp4

42:59

55 - Model Versioning with Weights and Biases and TensorFlow 2.mp4

16:29

12 - Human Emotions Detection

56 - data preparation.mp4

28:36

57 - Modeling and Training.mp4

51:01

58 - Data augmentation.mp4

17:45

59 - Tensorflow records.mp4

36:52

13 - Modern Convolutional Neural Networks

60 - Alexnet.mp4

17:03

61 - vggnet.mp4

11:13

62 - resnet.mp4

34:17

63 - coding resnet.mp4

22:01

64 - mobilenet.mp4

24:19

65 - efficientnet.mp4

17:29

14 - Transfer learning

66 - Pretrained Models.mp4

24:06

67 - Finetuning.mp4

13:05

15 - Understanding the blackbox

68 - visualizing intermediate layers.mp4

20:34

69 - gradcam method.mp4

21:15

16 - Class Imbalance and Ensembling

70 - Ensembling.mp4

05:41

71 - Class imbalance.mp4

13:26

17 - Transformers in Vision

72 - Understanding VITs.mp4

53:57

73 - Building VITs from scratch.mp4

51:18

74 - Finetuning Huggingface VITs.mp4

23:57

75 - Model Evaluation with Wandb.mp4

21:17

76 - Data efficient Transformers.mp4

08:58

77 - Swin Transformers.mp4

17:00

18 - Deploying the Image classification model

78 - Conversion from tensorflow to Onnx Model.mp4

25:10

79 - Understanding quantization.mp4

31:25

80 - Practical quantization of Onnx Model.mp4

08:50

81 - Quantization Aware training.mp4

17:49

82 - Conversion to tensorflowlite model.mp4

18:26

83 - How APIs work.mp4

19:54

84 - Building API with Fastapi.mp4

01:20:38

85 - Deploying API to the Cloud.mp4

12:24

86 - Load testing API.mp4

13:51

Description

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

What You'll Learn?

The Basics of Tensors and Variables with Tensorflow
Linear Regression, Logistic Regression and Neural Networks built from scratch.
Basics of Tensorflow and training neural networks with TensorFlow 2.
Convolutional Neural Networks applied to Malaria Detection
Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers
Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score
Classification Model Evaluation with Confusion Matrix and ROC Curve
Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing
Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation
Data augmentation with TensorFlow using TensorFlow image and Keras Layers
Advanced augmentation strategies like Cutmix and Mixup
Data augmentation with Albumentations with TensorFlow 2 and PyTorch
Custom Loss and Metrics in TensorFlow 2
Eager and Graph Modes in TensorFlow 2
Custom Training Loops in TensorFlow 2
Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling
Machine Learning Operations (MLOps) with Weights and Biases
Experiment tracking with Wandb
Hyperparameter tuning with Wandb
Dataset versioning with Wandb
Model versioning with Wandb
Human emotions detection
Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)
Transfer learning
Visualizing convnet intermediate layers
Grad-cam method
Model ensembling and class imbalance
Transformers in Vision
Huggingface Transformers
Vision Transformers
Model deployment
Conversion from tensorflow to Onnx Model
Quantization Aware training
Building API with Fastapi
Deploying API to the Cloud

Who is this for?

Beginner Python Developers curious about Applying Deep Learning for Computer vision

Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood

Anyone who wants to master deep learning fundamentals and also practice deep learning for image classification using best practices in TensorFlow.

Computer Vision practitioners who want to learn how state of art image classification models are built and trained using deep learning.

Anyone wanting to deploy image classification Models

Learners who want a practical approach to Deep learning for image classification

More details

Description
Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,...
With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.
In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface

You will learn:
The Basics of Tensorflow (Tensors, Model building, training, and evaluation)
Deep Learning algorithms like Convolutional neural networks and Vision Transformers
Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)
Mitigating overfitting with Data augmentation
Advanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard
Machine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)
Binary Classification with Malaria detection
Multi-class Classification with Human Emotions Detection
Transfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet)
Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!
This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Enjoy!!!

Who this course is for:
Beginner Python Developers curious about Applying Deep Learning for Computer vision
Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood
Anyone who wants to master deep learning fundamentals and also practice deep learning for image classification using best practices in TensorFlow.
Computer Vision practitioners who want to learn how state of art image classification models are built and trained using deep learning.
Anyone wanting to deploy image classification Models
Learners who want a practical approach to Deep learning for image classification

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