Deep Learning : Image Classification With Tensorflow In 2023

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

Requirements

Basic Knowledge of Python

Access to an internet connection, as we shall be using Google Colab (free version)

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 HuggingfaceYou will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine 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 DetectionTransfer 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!!!

Overview

Section 1: Introduction

Lecture 1 Welcome

Lecture 2 General Introduction

Section 2: Tensors and variables

Lecture 3 Basics

Lecture 4 Initialization and Casting

Lecture 5 Indexing

Lecture 6 Maths Operations

Lecture 7 Linear Algebra Operations

Lecture 8 Common Methods

Lecture 9 RaggedTensors

Lecture 10 Sparse Tensors

Lecture 11 String Tensors

Lecture 12 Variables

Section 3: [PRE-REQUISCITE] Building neural networks with Tensorflow

Lecture 13 Understanding the Task

Lecture 14 Data Preparation

Lecture 15 Linear Regression Model

Lecture 16 Error Sanctioning

Lecture 17 Training and Optimization

Lecture 18 Performance Measurement

Lecture 19 Validation and Testing

Lecture 20 Corrective Measures

Section 4: Building convnets with tensorflow

Lecture 21 Understanding the Task

Lecture 22 Data Preparation

Lecture 23 Data Visualization

Lecture 24 Data Processing

Lecture 25 How and Why Convolutional Neural Networks Work

Lecture 26 Building ConvNets with TensorFlow

Lecture 27 Binary Crossentropy Loss

Lecture 28 Training

Lecture 29 Model Evaluation and Testing

Lecture 30 Loading and Saving tensorflow models to gdrive

Section 5: Building more advanced TensorFlow Models with Functional API, Subclassing and Cu

Lecture 31 Functional API

Lecture 32 Model Subclassing

Lecture 33 Custom Layers

Section 6: Evaluating Classification Models

Lecture 34 Precision,Recall,Accuracy

Lecture 35 Confusion Matrix

Lecture 36 ROC curve

Section 7: Improving Model Performance

Lecture 37 Callbacks with TensorFlow,

Lecture 38 Learning Rate Scheduling,

Lecture 39 Model Checkpointing

Lecture 40 Mitigating Overfitting and Underfitting with Dropout, Regularization

Section 8: Data Augmentation

Lecture 41 Data augmentation with TensorFlow using tf.image and Keras Layers

Lecture 42 Mixup Data augmentation with TensorFlow 2 with intergration in tf.data

Lecture 43 Cutmix Data augmentation with TensorFlow 2 and intergration in tf.data

Lecture 44 Albumentations with TensorFlow 2 and PyTorch for Data augmentation

Section 9: Advanced Tensorflow

Lecture 45 Custom Loss and Metrics in TensorFlow 2

Lecture 46 Eager and Graph Modes in TensorFlow 2

Lecture 47 Custom Training Loops in TensorFlow 2

Section 10: Tensorboard integration with TensorFlow 2

Lecture 48 Log data

Lecture 49 view model graphs

Lecture 50 hyperparameter tuning

Lecture 51 Profiling and other visualizations with Tensorboard

Section 11: MLOps with Weights and Biases

Lecture 52 Experiment Tracking

Lecture 53 Hyperparameter Tuning with Weights and Biases and TensorFlow 2

Lecture 54 Dataset Versioning with Weights and Biases and TensorFlow 2

Lecture 55 Model Versioning with Weights and Biases and TensorFlow 2

Section 12: Human Emotions Detection

Lecture 56 data preparation

Lecture 57 Modeling and Training

Lecture 58 Data augmentation

Lecture 59 Tensorflow records

Section 13: Modern Convolutional Neural Networks

Lecture 60 Alexnet

Lecture 61 vggnet

Lecture 62 resnet

Lecture 63 coding resnet

Lecture 64 mobilenet

Lecture 65 efficientnet

Section 14: Transfer learning

Lecture 66 Pretrained Models

Lecture 67 Finetuning

Section 15: Understanding the blackbox

Lecture 68 visualizing intermediate layers

Lecture 69 grad-cam method

Section 16: Class Imbalance and Ensembling

Lecture 70 Ensembling

Lecture 71 Class imbalance

Section 17: Transformers in Vision

Lecture 72 Understanding VITs

Lecture 73 Building VITs from scratch

Lecture 74 Finetuning Huggingface VITs

Lecture 75 Model Evaluation with Wandb

Lecture 76 Data efficient Transformers

Lecture 77 Swin Transformers

Section 18: Deploying the Image classification model

Lecture 78 Conversion from tensorflow to Onnx Model

Lecture 79 Understanding quantization

Lecture 80 Practical quantization of Onnx Model

Lecture 81 Quantization Aware training

Lecture 82 Conversion to tensorflowlite model

Lecture 83 How APIs work

Lecture 84 Building API with Fastapi

Lecture 85 Deploying API to the Cloud

Lecture 86 Load testing API

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