Deep Learning : Convolutional Neural Networks With Python

Deep Learning and Computer Vision using Convolutional Neural Networks with Python, Pytorch. Train, Test, Deploy Models

What you'll learn

Deep Convolutional Neural Networks with Python and Pytorch Basics to Expert

Introduction to Deep Learning and its Building Blocks Artificial Neurons

Define Convolutional Neural Network Architecture from Scratch with Python and Pytorch

Hyperparameters Optimization For Convolutional Neural Networks to Improve Model Performance

Custom Datasets with Augmentations to Increase Image Data Variability

Training and Testing Convolutional Neural Network using Pytorch

Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Advanced CNNs for Segmentation, Object tracking, and Pose Estimation.

Pretrained Convolutional Neural Networks and their Applications

Transfer Learning using Convolutional Neural Networks Models

Convolutional Neural Networks Encoder Decoder Architectures

YOLO Convolutional Neural Networks for Computer Vision Tasks

Region-based Convolutional Neural Networks for Object Detection

Requirements

A Google Gmail account is required to get started with Google Colab to write Python Code

Python Programming experience is an advantage but not required

Description

Are you ready to unlock the power of deep learning and revolutionize your career? Dive into the captivating realm of Deep Learning with our comprehensive course Deep Learning: Convolutional Neural Networks (CNNs) using Python and Pytorch. Discover the power and versatility of CNNs, a cutting-edge technology revolutionizing the field of artificial intelligence. With hands-on Python tutorials, you'll unravel the intricacies of CNN architectures, mastering their design, implementation, and optimization. One of the key advantages of deep CNN is its ability to automatically learn features at different levels of abstraction. Lower layers of the network learn low-level features, such as edges or textures, while higher layers learn more complex and abstract features. This hierarchical representation allows deep learning models to capture and understand complex patterns in the data, enabling them to excel in tasks such as image recognition, natural language processing, speech recognition, and many others.Introducing our comprehensive deep CNNs with python course, where you'll dive deep into Convolutional Neural Networks and emerge with the skills you need to succeed in the modern era of AI. Computer Vision refers to AI algorithms designed to extract knowledge from images or videos. Computer vision is a field of artificial intelligence (AI) that enables computers to understand and interpret visual information from digital images or videos. It involves developing deep learning algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from visual data, much like the human visual system. Convolutional Neural Networks (CNNs) are most commonly used Deep Learning technique for computer vision tasks. CNNs are well-suited for processing grid-like input data, such as images, due to their ability to capture spatial hierarchies and local patterns.In today's data-driven world, Convolutional Neural Networks  stand at the forefront of image recognition, object detection, and visual understanding tasks. Understanding CNNs is not only essential for aspiring data scientists and machine learning engineers but also for professionals seeking to leverage state-of-the-art technology to drive innovation in various domains. From self-driving cars and medical imaging to facial recognition and augmented reality, CNNs find applications across diverse industries. Whether you're interested in revolutionizing healthcare, enhancing autonomous systems, or developing cutting-edge computer vision applications, this course equips you with the knowledge and skills to excel in any CNN-related endeavor.Course Key Learning Outcomes:Deep Convolutional Neural Networks with Python and Pytorch Basics to ExpertIntroduction to Deep Learning and its Building Blocks Artificial NeuronsDefine Convolutional Neural Network Architecture from Scratch with Python and PytorchHyperparameters Optimization For Convolutional Neural Networks to Improve Model PerformanceCustom Datasets with Augmentations to Increase Image Data VariabilityTraining and Testing Convolutional Neural Network using PytorchPerformance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNsVisualize Confusion Matrix and Calculate Precision, Recall, and F1 ScoreAdvanced CNNs for Segmentation, Object tracking, and Pose Estimation.Pretrained Convolutional Neural Networks and their ApplicationsTransfer Learning using Convolutional Neural Networks ModelsConvolutional Neural Networks Encoder Decoder ArchitecturesYOLO Convolutional Neural Networks for Computer Vision TasksRegion-based Convolutional Neural Networks for Object DetectionIn this comprehensive course you will start from building Deep Convolutional Neural Networks  architecture from scratch with Dataset Augmentation with different transformations to increase image variability , HyperParameteres Optimization before training the model to improve performance, Model validation on Test Images, Performance metrics calculation including Accuracy, Precision, Recall, F1 score and Confusion matrix visualization to see detailed insights into the model's performance, beyond simple metrics. Then you will move forward to advanced CNN Architectures Including RESNT, ALEXNET for Images Classification, UNET, PSPNET encoder decoder Architectures for semantic segmentation, Region based CNN for OD and YOLO CNNs for real time object Detection, classification instance segmentation, object tracking, and pose estimation.Join us on this exciting journey, where you'll not only grasp the core concepts but also unlock the door to advanced CNN architectures, equipping yourself with the skills needed to conquer the most challenging computer vision tasks with confidence and expertise. You will follow a complete pipeline to deep dive into CNN for real world applications. I will provide you the complete python code to build, train, test, and deploy CNN from scratch for different Artificial Intelligence tasks.Don't miss out on this incredible opportunity to take your skills to the next level. Enroll now and join the thousands of students who've already transformed their careers with our courses. “ Thank you and see you inside the class" !

Overview

Section 1: Introduction to Course

Lecture 1 Introduction

Section 2: Artificial Neurons - The building blocks of Deep Learning

Lecture 2 Introduction to Deep Learning and Artificial Neurons

Section 3: Introduction to Convolutional Neural Networks (CNNs)

Lecture 3 Introduction to Convolutional Neural Networks (CNNs)

Section 4: Google Colab Environment Set-up for Writing Python Code

Lecture 4 Google Colab Environment for Writing Python and Pytorch Code

Section 5: Convolutional Neural Networks from Scratch using Python

Lecture 5 Define Convolutional Neural Network Architecture from Scratch using Python

Section 6: Dataset and its Augmentation

Lecture 6 Dataset and its Augmentation

Section 7: Hyperparameters Optimization For Convolutional Neural Networks

Lecture 7 Hyperparameters Optimization For Training Models

Section 8: Training Convolutional Neural Network from Scratch

Lecture 8 Training Convolutional Neural Network from Scratch

Section 9: Validating Convolutional Neural Network on Test Images

Lecture 9 Validating Convolutional Neural Network on Test Images

Section 10: Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Lecture 10 Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Section 11: Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Lecture 11 Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Section 12: Resources: Python Code for Convolutional Neural Networks from Scratch

Lecture 12 Resources: Python Code for Convolutional Neural Networks from Scratch

Section 13: Pretrained Convolutional Neural Networks

Lecture 13 Pretrained Convolutional Neural Networks with Python

Lecture 14 Python Code to use the Pretrained CNN Models

Section 14: Transfer Learning using Convolutional Neural Networks

Lecture 15 What is Transfer Learning

Lecture 16 Transfer Learning by Fine Tuning CNNs Models

Lecture 17 Transfer Learning with CNNs Models as Fixed Feature Extractor

Lecture 18 Transfer Learning Python, Pytorch Code and Dataset

Section 15: Convolutional Neural Networks Encoder Decoder Architectures

Lecture 19 Convolutional Neural Networks Based Encoders

Lecture 20 Convolutional Neural Networks Based Decoders

Lecture 21 Multi-Task Contextual Encoder Decoder Network

Section 16: YOLO Convolutional Neural Networks

Lecture 22 YOLO Convolutional Neural Networks Architecture

Lecture 23 How YOLO Works to Identify Objects

Section 17: Region-based Convolutional Neural Networks

Lecture 24 Region-based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN)

Lecture 25 Detectron2 for Ojbect Detection with PyTorch

Lecture 26 Perform Object Detection using Detectron2 Models

Lecture 27 Resources: Python and PyTorch Code for Object Detection

This course is designed for individuals with a keen interest in Deep Learning and Convolutional Neural Networks (CNNs) with Python and Pytorch to solve Real-World AI Problems.,Whether you're a beginner looking to build a strong foundation in Computer Vision, Object Tracking, Segmentation, Pose Estimation, Classification, Object Detection or an experienced professional aiming to enhance your skills, this course provides valuable insights and hands-on experience with CNNs.