Deep Learning and Neural Networks with Python Zero to Expert

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Computer Science & AI School,Mazhar Hussain

9:17:07

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1. Introduction

1. Introduction.mp4

02:57

2. Introduction to Deep Learning

1. Introduction to Deep Learning vs Machine Learning.mp4

14:03

2. Building Blocks of Deep Learning - Artificial Neurons.mp4

07:46

3. Artificial Neural Networks with Python

1. Introduction to Neural Networks.html

2. Perceptron -- Building Block of Neural Networks.html

3. Colab for Writing Python Code.mp4

07:24

4. Convolutional Neural Networks with Python from Scratch

1. Introduction to Convolutional Neural Networks (CNNs).mp4

08:14

2. Coding Convolutional Neural Networks from Scratch with Python.mp4

20:06

3. Develop CNN with Python and Pytroch Code from Scratch.html

4. Dataset and its Augmentation.mp4

07:09

5. Pytorch Code for Data Loading and Augmentation.html

6. Hyperparameters Optimization For Convolutional Neural Networks.mp4

10:01

7. CNN Optimization with Pytorch and Python Code.html

8. Training Convolutional Neural Network from Scratch.mp4

04:03

9. CNN Training with Python and Pytorch Code.html

10. Validating Convolutional Neural Network on Test Images.mp4

02:45

11. CNN Testing with Pytorch and Python Code.html

12. Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs.mp4

04:10

13. Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score.mp4

08:20

14. Performance Metrics Calculation with Python and Pytorch Code.html

15.1 CNN from Scratch Code.zip

15. Resources Python Code for Convolutional Neural Networks from Scratch.html

5. Deep Convolutional Neural Networks with Python for Image Classification

1. Coding DEEP CNN from Scratch for Image Classification.mp4

12:28

2. Optimize, Train and Test Deep CNN with Improved Performance.mp4

10:50

3. Deep CNN Python and Pytroch Code.html

6. Deep Learning Pretrained Models with Python

1. PreTrained Deep Learning Models Importance.mp4

05:11

2. Deep Learning ResNet and AlexNet Architectures.mp4

04:58

3. Read Data from Google Drive to Colab Notebook.mp4

02:44

4. Perform Data Preprocessing.mp4

05:00

5. Use ResNet and AlexNet PreTrained Models.mp4

08:03

6.1 imagenet1000Classes.txt

6.2 pizza.zip

6.3 single-label classification.zip

6. Python Code for Pretrained ResNet and AlexNet.html

7. Transfer Learning with Python

1. Why Transfer Learning.mp4

06:09

2. Data Augmentation, Dataloaders, and Training Function.mp4

07:20

3. FineTuning Deep ResNet Model.mp4

07:19

4. Optimization of ResNet Model HyperParameteres.mp4

06:22

5. Deep ResNet Model Training.mp4

03:34

6. Deep ResNet Model as Fixed Feature Extractor.mp4

04:42

7. Results and Training of Model as Fixed Feature Extractor.mp4

01:41

8. Deep Learning for Object Detection on Custom Dataset

1. Introduction to Object Detection.mp4

06:38

2. Overview of CNN, RCNN, Fast RCNN, and Faster RCNN.mp4

25:12

3. Detectron2 for Ojbect Detection with PyTorch.mp4

18:10

4. Perform Object Detection using Detectron2 Pretrained Models.mp4

10:41

5.1 Python and PyTorch Code for Object Detection using Detectron2.zip

5. Python and PyTorch Code for Object Detection using Detectron2.html

6. Custom Dataset for Object Detection.mp4

12:18

7.1 Balloon Dataset.zip

7. Balloon Dataset.html

8. Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset.mp4

13:32

9.1 Python and Pytroch Code for Object Detection On Custom Dataset.zip

9. Python and Pytroch Code for Object Detection On Custom Dataset.html

9. Deep Learning for Video Object Detection

1. What is YOLO .mp4

06:26

2. How YOLO works for Object Detection .mp4

06:15

3. YOLOv8 Introduction and Architecture.mp4

13:36

4. Custom Vehicles Detection Dataset.mp4

06:29

5. HyperParameters Settings for YOLO8.mp4

07:10

6. Training YOLO8 on Vehicles Dataset.mp4

05:48

7. Testing YOLO8 on Videos and Images.mp4

07:12

8. Calculate Performance Metrics (Precision, Recall, Mean Average Precision mAP).mp4

06:37

9. Deploy YOLO8.mp4

03:11

10.1 TestVideo.zip

10.2 VehiclesDetection Dataset.zip

10.3 vehiclesdetectioncode.zip

10. Resources Videos Vehicles Detection Complete Code and Dataset.html

10. Deep Learning for Pose Estimation with Python

1. Introduction to Pose Estimation.html

2. Pose Estimation and Key Points Detection with Python.html

11. Deep Learning for Instance Segmentation

1. What is Instance Segmentation.mp4

04:35

2. Deep Learning Architecture Mask RCNN for Instance Segmentation.mp4

04:24

3. Instance Segmentation PyTorch Facebook Library.mp4

11:05

4. Custom Dataset for Instance Segmentation.mp4

12:18

5. Train, Evaluate & Visualize Instance Segmentation on Custom Dataset.mp4

18:17

6.1 Balloon Dataset.zip

6.2 Python and Pytorch Code of Instance Segmentation on Custom Dataset.zip

6. Resources Code and Dataset for Instance Segmentation.html

12. Deep Learning for Semantic Segmentation

1. Introduction to Semantic Segmentation.mp4

05:31

2. Semantic Segmentation Real-world Applications.mp4

11:02

3. Pyramid Scene Parsing Network for Segmentation.mp4

04:30

4. UNet Architecture for Segmentation.mp4

03:37

5. Pyramid Attention Network for Segmentation.mp4

03:34

6. Multi-Task Contextual Network for Segmentation.mp4

04:37

7. Datasets for Semantic Segmentation.mp4

05:52

8. Data Annotations Tool for Semantic Segmentation.mp4

05:29

9.1 TrayDataset for Segmentation.zip

9. Dataset for Semantic Segmentation.html

10. Data Loading with PyTorch Customized Dataset Class.mp4

18:01

11. Data Augmentation using Albumentations with different Transformations.mp4

09:49

12. Data Loaders Implementation in Pytorch.mp4

05:01

13. Performance Metrics (IOU, Pixel Accuracy) for Segmentation Models Evaluation.mp4

09:48

14. Learn Transfer Learning with Deep Resnet Architecture.mp4

08:49

15. Encoders for Segmentation in PyTorch.mp4

09:38

16. Decoders for Segmentation in PyTorch.mp4

10:04

17. Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++).mp4

17:00

18. Hyperparameters Optimization of Segmentation Models.mp4

08:31

19. Training of Segmentation Models.mp4

09:00

20. Test & Deploy Segmentation Models and Calculate Class-wise IOU, Accuracy, Fscore.mp4

12:01

21. Visualize Segmentation Results and Generate RGB Output Segmentation Map.mp4

12:00

22.1 Final Code.zip

22.2 TrayDataset for Segmentation.zip

22. Resources Code and Dataset of Segmentation with Deep Learning.html

Description

Deep Learning with Python for Classification, Semantic and Instance Segmentation, Pose Estimation, and Object Detection

What You'll Learn?

Deep Learning with Python and Pytorch Complete Guide
Machine Learning to Deep Learning Paradigm Shift Key Concepts
Artificial Deep Neural Networks Coding from Scratch in Python
Deep Convolutional Neural Networks Coding from Scratch in Python
Transfer Learning with Deep Pretrained Models using Python
Deep Learning for Image Classification with Python
Deep Learning for Pose Estimation with Python
Deep Learning for Instance Segmentation with Python
Deep Learning for Semantic Segmentation with Python
Deep Learning for Object Detection with Python
Train, Test and Deploy Deep Learning Models for Real-world Applications
Calculate Performance Metrics (Accuracy, Precision, Recall, IOU) with Python

Who is this for?

This course is designed for AI enthusiasts looking to build a solid foundation in Deep Learning with Python.

Data Scientists, Computer Vision Engineers, Software Engineers, and AI researchers seeking to enhance their expertise in Deep Learning.

What You Need to Know?

You will learn everything you need to know starting from Deep Learning with Python basics to advanced.

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

More details

Description
Unlock the power of artificial intelligence with our comprehensive course, "Deep Learning with Python ." This course is designed to transform your understanding of machine learning and take you on a journey into the world of deep learning. Whether you're a beginner or an experienced programmer, this course will equip you with the essential skills and knowledge to build, train, and deploy deep learning models using Python and PyTorch. Deep learning is the driving force behind groundbreaking advancements in generative AI, robotics, natural language processing, image recognition, and artificial intelligence. By enrolling in this course, youâ€™ll gain practical knowledge and hands-on experience in applying Python skills to deep learning
Course Outline
Introduction to Deep Learning
Understanding the paradigm shift from machine learning to deep learning
Key concepts of deep learning
Setting up the Python environment for deep learning
Artificial Deep Neural Networks: Coding from Scratch in Python
Fundamentals of artificial neural networks
Building and training neural networks from scratch
Implementing forward and backward propagation
Optimizing neural networks with gradient descent
Deep Convolutional Neural Networks: Coding from Scratch in Python
Introduction to convolutional neural networks (CNNs)
Building and training CNNs from scratch
Understanding convolutional layers, pooling, and activation functions
Applying CNNs to image data
Transfer Learning with Deep Pretrained Models using Python
Concept of transfer learning and its benefits
Using pretrained models for new tasks
Fine-tuning and adapting pretrained models
Practical applications of transfer learning
Deep Learning for Image Classification with Python
Techniques for image classification
Building image classification models
Evaluating and improving model performance
Deploying image classification models
Deep Learning for Pose Estimation with Python
Introduction to pose estimation
Building and training pose estimation models
Using deep learning for human pose estimation
Deep Learning for Instance Segmentation with Python
Understanding instance segmentation
Building and training instance segmentation models
Techniques for segmenting individual objects in images
Deep Learning for Semantic Segmentation with Python
Fundamentals of semantic segmentation
Building and training semantic segmentation models
Techniques for segmenting images into meaningful parts
Real-world applications of Semantic segmentation
Deep Learning for Object Detection with Python
Introduction to object detection
Building and training object detection models
Techniques for detecting and localizing objects in images
Practical use cases and deployment
Who Should Enroll?
Beginners: Individuals with basic programming knowledge who are eager to dive into deep learning.
Intermediate Learners: Those who have some experience with machine learning and wish to advance their skills in deep learning and PyTorch.
Professionals: Data scientists, AI researchers, and software engineers looking to enhance their expertise in deep learning and apply it to real-world problems.
What You'll Gain
A solid foundation in deep learning concepts and techniques
Hands-on experience in building and training various deep learning models from scratch
Proficiency in using Python and PyTorch for deep learning applications
The ability to implement and fine-tune advanced models for image classification, pose estimation, segmentation, and object detection
Practical knowledge to deploy deep learning models in real-world scenarios
Why Choose This Course?
Comprehensive Content: Covers a wide range of deep learning topics and applications.
Hands-on Projects: Practical coding exercises and real-world projects to solidify your understanding.
Expert Guidance: Learn from experienced instructors with deep expertise in deep learning and Python.
Flexible Learning: Access the course materials anytime, anywhere, and learn at your own pace.
Enroll now and embark on your journey to mastering deep learning with Python and PyTorch. Transform your skills and open up new career opportunities in the exciting field of artificial intelligence!

See you inside the course!!
Who this course is for:
This course is designed for AI enthusiasts looking to build a solid foundation in Deep Learning with Python.
Data Scientists, Computer Vision Engineers, Software Engineers, and AI researchers seeking to enhance their expertise in Deep Learning.

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Computer Science & AI School aims to open the door to sought-after technology careers for you by learning cutting edge Computer Science courses in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming and Databases with course material ranges from entry-level to specialized topics. You will be able to update your marketable and competitive skills through commercial applications of computing practices. You’ll master in-demand computing skills, solve complex problems, and hone your innovation and creativity. The hands-on exercises and project-based approach will help develop the technical and transferable skills needed for a fulfilling career in your field.

Mazhar Hussain

Instructor's Courses

Mazhar Hussain is teaching Computer Science courses since 2015 at the National University of Computer and Emerging Sciences. He holds a Master's Degree in Computer Science and is passionate to deliver practical knowledge and skills to his students. He has been teaching courses in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming, and Databases especially SQL SERVER, MYSQL, ORACLE, and MS ACCESS for more than 5 years span. He has been working as a developer in the Microsoft Innovation Center and is now taking all that he has learned to help you discover amazing career opportunities. Please do not hesitate if you have any questions, I am always available for your help at any time to transform a passionate, enthusiastic learner into a skilled person.

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