Udemy – Deep Learning Masterclass with TensorFlow 2 Over 20 Projects 2023-2 – Download

Explanation

Deep Learning Masterclass with TensorFlow 2 Over 20 Projects, Deep Learning is one of the most popular areas in computer science today. It has applications in many different areas. With the publication of highly effective deep learning models in the early 2010s, we have seen significant advances in the state of the art in areas such as Computer Vision, Natural Language Processing, Image Processing, and Signal Processing. The demand for deep learning engineers is skyrocketing, and experts in these fields are highly paid because of their value. However, starting this site is not easy. There is a lot of information out there, most of which is outdated and often not considered by beginners.

In this course, we will take you on an amazing journey where you will learn different concepts in a step-by-step and project-based approach. You should use Tensorflow 2 (the world’s most popular deep learning library, built by Google) and Huggingface. We will start by understanding how to build very simple models (such as linear models for car price prediction, text classification for movie reviews, binary classification for malaria prediction) using Tensorflow and Huggingface transformers, advanced models (such as object detection models YOLO , Elemental Element Vocabulary with GPT2 and Image Generation for GANs

What will you learn?

Fundamentals of Tensors and Transformations with Tensorflow
Tensorflow basics and training neural networks in TensorFlow 2.
Convolutional Neural Networks Applied to Malaria Detection
Building advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers
Evaluation of Classification Models using various parameters such as: Accuracy, Recall, Precision and F1-score.
A Confusion Matrix Assessment Classification Model and ROC Curve
Tensorflow Recursive, Learning Level Scheduling and Model Checking
Reduction of excess and adaptation to omission, consistency, data collection
Data mining in TensorFlow using TensorFlow graphs and Keras layers
Advanced upscaling techniques such as Cutmix and Mixup
Data mining with TensorFlow 2 and PyTorch Albumentations
Specific Cons and Metrics for TensorFlow 2
TensorFlow 2’s interesting and graphical methods
Custom Training Loops for TensorFlow 2
Integrate TensorFlow 2 with Tensorboard for data logging, model graph viewing, hyperparameter tuning and visualization.

Who is this course for?

Beginning Python developers are very interested in applying Deep Learning to Computer Vision and Natural Language Processing.
Deep learning of computer vision Professionals with experience of how things work underneath
Anyone who wants to learn the basics of deep learning and also practice deep learning in computer vision using TensorFlow best practices.
Computer Vision professionals who want to learn how state-of-the-art computer vision models are built and trained using deep learning.
Natural Language Processing professionals who want to learn how state of the art NLP models are built and trained using deep learning.
Anyone who wants to deploy ML Models
Students who want a practical approach to deep learning in computer vision, Natural Language Processing and speech recognition.