Udemy – Deep Learning Masterclass with TensorFlow 2 Over 20 Projects 2024-4

Deep Learning Masterclass with TensorFlow 2 Over 20 Projects, Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing.  The demand for Deep Learning 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 There’s so much information out there, much of which is outdated and many times don’t take the beginners into consideration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, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)

What you’ll learn

• The Basics of Tensors and Variables with Tensorflow
• 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

Who this course is for

• Beginner Python Developers curious about Applying Deep Learning for Computer vision and Natural Language Processing
• 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.
• Natural Language Processing practitioners who want to learn how state of art NLP 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, Natural Language Processing and Sound recognition

Specificatoin of Deep Learning Masterclass with TensorFlow 2 Over 20 Projects

• Publisher : Udemy
• Teacher : Neuralearn Dot AI
• Language : English
• Level : All Levels
• Number of Course : 213
• Duration : 67 hours and 10 minutes

Content

Requirements

• Basic Math
• Access to an internet connection, as we shall be using Google Colab (free version)
• Basic Knowledge of Python

Pictures

Sample Clip

Installation Guide

Extract the files and watch with your favorite player

Subtitle : English

Quality: 720p

The 2024/4 version has a reduction of 100 lessons and a duration of 32 hours and 26 minutes compared to the 2023/2 version.

Download Links

Download Part 1 – 4 GB

Download Part 2 – 4 GB

Download Part 3 – 4 GB

Download Part 4 – 4 GB

Download Part 5 – 4 GB

Download Part 6 – 4 GB

Download Part 7 – 2.17 GB

File size

26.1 GB