Master Deep Learning for Computer Vision in TensorFlow[2024] | Udemy
English | Size: 22.21 GB
Genre: eLearning[/center]
Use ConvNets & Vision Transformers to build projects in Image classification,generation,segmentation & Object detection
What you'll 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)
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't easy. There's so much information out there, much of which is outdated and many times don't take the beginners into consideration 
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!!!
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 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.
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