Master Deep Learning for Computer Vision in TensorFlow[2024]

Master Deep Learning for Computer Vision in TensorFlow[2024], available at $49.99, has an average rating of 4.63, with 235 lectures, based on 147 reviews, and has 1399 subscribers.

You will learn about The Basics of Tensors and Variables with Tensorflow Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle. 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 Model deployment Conversion from tensorflow to Onnx Model Quantization Aware training Building API with Fastapi Deploying API to the Cloud Object detection from scratch with YOLO Image Segmentation from scratch with UNET model People Counting from scratch with Csrnet Digit generation with Variational autoencoders (VAE) Face generation with Generative adversarial neural networks (GAN) This course is ideal for individuals who are Beginner Python Developers curious about Applying Deep Learning for Computer vision or Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood or Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow. or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning. or Anyone wanting to deploy ML Models or Learners who want a practical approach to Deep learning for Computer vision or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning. It is particularly useful for Beginner Python Developers curious about Applying Deep Learning for Computer vision or Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood or Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow. or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning. or Anyone wanting to deploy ML Models or Learners who want a practical approach to Deep learning for Computer vision or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.

Enroll now: Master Deep Learning for Computer Vision in TensorFlow[2024]

Summary

Title: Master Deep Learning for Computer Vision in TensorFlow[2024]

Price: $49.99

Average Rating: 4.63

Number of Lectures: 235

Number of Published Lectures: 146

Number of Curriculum Items: 235

Number of Published Curriculum Objects: 146

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

The Basics of Tensors and Variables with Tensorflow

Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.

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

Model deployment

Conversion from tensorflow to Onnx Model

Quantization Aware training

Building API with Fastapi

Deploying API to the Cloud

Object detection from scratch with YOLO

Image Segmentation from scratch with UNET model

People Counting from scratch with Csrnet

Digit generation with Variational autoencoders (VAE)

Face generation with Generative adversarial neural networks (GAN)

Who Should Attend

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 computer vision using best practices in TensorFlow.

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

Anyone wanting to deploy ML Models

Learners who want a practical approach to Deep learning for Computer vision

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

Target Audiences

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 computer vision using best practices in TensorFlow.

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

Anyone wanting to deploy ML Models

Learners who want a practical approach to Deep learning for Computer vision

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

Deep Learning is a hot topic today! This is because of the impact it’s having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation & People Counting

To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.

Some applications of Computer Vision are:

Helping doctors more efficiently carry out medical diagnostics

enabling farmers to harvest their products with robots, with  the need for very little human intervention,

Enable self-driving cars

Helping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brain

Creation of art with GANs, VAEs, and Diffusion Models

Data analytics in sports, where players’ movements are monitored automatically using sophisticated computer vision algorithms.

The demand for Computer Vision engineers is skyrocketing and experts in this field are highly paid, because of their value.However, getting started in this field isn鈥檛 easy. There鈥檚 so much information out there, much of which is outdated and many times don’t take the beginners into consideration 馃檨

In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world’s most popular library for deep learning, built by Google) and Huggingface.We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and binary classifier for malaria prediction) using Tensorflow to much more advanced models (like object detection model with YOLO and Image generation with GANs).

After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep learning for computer vision solutions that big tech companies encounter.

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) and Vision Transformers (VITs)

Object Detection with YOLO (You Only Look Once)

Image Segmentation with UNet

People Counting with Csrnet

Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)

Digit generation with Variational Autoencoders

Face generation with Generative Adversarial Neural Networks

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!!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: General Introduction

Lecture 3: Course Content

Lecture 4: Link to Code

Chapter 2: Tensors and Variables

Lecture 1: Link to Code

Lecture 2: Tensor Basics

Lecture 3: Tensor Initialization and Casting

Lecture 4: Indexing

Lecture 5: Maths Operations in Tensorflow

Lecture 6: Linear Algebra Operations in Tensorflow

Lecture 7: Common Tensorflow Methods

Lecture 8: Ragged Tensors

Lecture 9: Sparse Tensors

Lecture 10: String Tensors

Lecture 11: Tensorflow Variables

Chapter 3: Building a Simple Neural Network in Tensorflow

Lecture 1: Link to Code

Lecture 2: Link to Dataset

Lecture 3: Task Understanding

Lecture 4: Data Preparation

Lecture 5: Linear Regression Model

Lecture 6: Error Sanctioning

Lecture 7: Training and Optimization

Lecture 8: Performance Measurement

Lecture 9: Validation and Testing

Lecture 10: Corrective Measures

Lecture 11: TensorFlow Datasets

Chapter 4: Building Convolutional Neural Networks [Malaria Diagnosis]

Lecture 1: Link to Code

Lecture 2: Task Understanding

Lecture 3: Data Preparation

Lecture 4: Data Visualization

Lecture 5: Data Processing

Lecture 6: How and Why Convolutional Neural Networks work

Lecture 7: Building Convnets in Tensorflow

Lecture 8: Binary Crossentropy Loss

Lecture 9: Convnet Training

Lecture 10: Model Evaluation and Testing

Lecture 11: Loading and Saving Tensorflow Models to Google Drive

Chapter 5: Building more advanced Models with Functional API, Subclassing and Custom Layers

Lecture 1: Functional API

Lecture 2: Model Subclassing

Lecture 3: Custom Layers

Chapter 6: Evaluating Classification Models

Lecture 1: Precision,Recall and Accuracy

Lecture 2: Confusion Matrix

Lecture 3: ROC Curve

Chapter 7: Improving Model Performance

Lecture 1: Tensorflow Callbacks

Lecture 2: Learning rate scheduling

Lecture 3: Model checkpointing

Lecture 4: Mitigating Overfitting and Underfitting with Dropout, Regularization

Chapter 8: Data Augmentation

Lecture 1: Data augmentation with TensorFlow using tf.image and Keras Layers

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

Lecture 3: Cutmix Data augmentation with TensorFlow 2 and intergration in tf.data

Lecture 4: Albumentations with TensorFlow 2 and PyTorch for Data augmentation

Chapter 9: Advanced Tensorflow Concepts

Lecture 1: Custom Loss and Metrics

Lecture 2: Eager and Graph Modes

Lecture 3: Custom Training Loops

Chapter 10: Tensorboard Integration

Lecture 1: Data Logging

Lecture 2: Viewing Model Graphs

Lecture 3: Hyperparameter tuning

Lecture 4: Profiling and other visualizations with Tensorboard.

Chapter 11: MLOps with Weights and Biases

Lecture 1: Experiment Tracking

Lecture 2: Hyperparameter Tuning with Weights and Biases and TensorFlow 2

Lecture 3: Dataset Versioning with Weights and Biases and TensorFlow 2

Lecture 4: Data Versioning with Wandb

Lecture 5: Model Versioning with Weights and Biases and TensorFlow 2

Chapter 12: Human Emotions Detection

Lecture 1: Link to Code

Lecture 2: Data Preparation

Lecture 3: Modeling and Training

Lecture 4: Data augmentation

Lecture 5: Tensorflow Records

Chapter 13: Modern Convolutional Neural Networks

Lecture 1: Alexnet

Lecture 2: Vggnet

Lecture 3: Resnet

Lecture 4: Coding Resnet

Lecture 5: Mobilenet

Lecture 6: Efficientnet

Chapter 14: Transfer Learning

Lecture 1: Leveraging Pretrained Models

Lecture 2: Finetuning

Chapter 15: Diving into the blackbox

Lecture 1: Visualizing intermediate layers

Lecture 2: Grad-cam Method

Chapter 16: Ensembling and class imbalance

Lecture 1: Ensembling

Lecture 2: Class Imbalance

Chapter 17: Transformers in Vision

Lecture 1: Understanding VITs

Lecture 2: Building VITs from scratch

Lecture 3: Finetuning Huggingface Transformers

Instructors

Neuralearn Dot AI
Helping millions of learners, master Deep Learning.

Rating Distribution

1 stars: 7 votes

2 stars: 5 votes

3 stars: 15 votes

4 stars: 30 votes

5 stars: 90 votes

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