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Description
Welcome to the world of Deep Learning! This course is designed to equip you with the knowledge and skills needed to excel in this exciting field. Whether you’re a Machine Learning practitioner seeking to advance your skillset or a complete beginner eager to explore the potential of Deep Learning, this course caters to your needs.
What You’ll Learn:
Master the fundamentals of Deep Learning, including Tensorflow and Keras libraries.
Build a strong understanding of core Deep Learning algorithms like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).
Gain practical experience through hands-on projects covering tasks like image classification, object detection, and image captioning.
Explore advanced topics like transfer learning, data augmentation, and cutting-edge models like YOLOv8 and Stable Diffusion.
The course curriculum is meticulously structured to provide a comprehensive learning experience:
Section 1: Computer Vision Introduction & Basics: Provides a foundation in computer vision concepts, image processing basics, and color spaces.
Section 2: Neural Networks – Into the World of Deep Learning: Introduces the concept of Neural Networks, their working principles, and their application to Deep Learning problems.
Section 3: Tensorflow and Keras: Delves into the popular Deep Learning frameworks, Tensorflow and Keras, explaining their functionalities and API usage.
Section 4: Image Classification Explained & Project: Explains Convolutional Neural Networks (CNNs), the workhorse for image classification tasks, with a hands-on project to solidify your understanding.
Section 5: Keras Preprocessing Layers and Transfer Learning: Demonstrates how to leverage Keras preprocessing layers for data augmentation and explores the power of transfer learning for faster model development.
Section 6: RNN LSTM & GRU Introduction: Provides an introduction to Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) for handling sequential data.
Section 7: GANS & Image Captioning Project: Introduces Generative Adversarial Networks (GANs) and their applications, followed by a project on image captioning showcasing their capabilities.
Section 9: Object Detection Everything You Should Know: Delves into object detection, covering various approaches like two-step detection, RCNN architectures (Fast RCNN, Faster RCNN, Mask RCNN), YOLO, and SSD.
Section 10: Image Annotation Tools: Introduces tools used for image annotation, crucial for creating labeled datasets for object detection tasks.
Section 11: YOLO Models for Object Detection, Classification, Segmentation, Pose Detection: Provides in-depth exploration of YOLO models, including YOLOv5, YOLOv8, and their capabilities in object detection, classification, segmentation, and pose detection. This section includes a project on object detection using YOLOv5.
Section 12: Segmentation using FAST-SAM: Introduces FAST-SAM (Segment Anything Model) for semantic segmentation tasks.
Section 13: Object Tracking & Counting Project: Provides an opportunity to work on a project involving object tracking and counting using YOLOv8.
Section 14: Human Action Recognition Project: Guides you through a project on human action recognition using Deep Learning models.
Section 15: Image Analysis Models: Briefly explores pre-trained models for image analysis tasks like YOLO-WORLD and Moondream1.
Section 16: Face Detection & Recognition (AGE GENDER MOOD Analysis): Introduces techniques for face detection and recognition, including DeepFace library for analyzing age, gender, and mood from images.
Section 17: Deepfake Generation: Provides an overview of deepfakes and how they are generated.
Section 18: BONUS TOPIC: GENERATIVE AI – Image Generation Via Prompting – Diffusion Models: Introduces the exciting world of Generative AI with a focus on Stable Diffusion models, including CLIP, U-Net, and related tools and resources.
What Sets This Course Apart:
Up-to-date Curriculum: This course incorporates the latest advancements in Deep Learning, including YOLOv8, Stable Diffusion, and Fast-SAM.
Hands-on Projects: Apply your learning through practical projects, fostering a deeper understanding of real-world applications.
Clear Explanations: Complex concepts are broken down into easy-to-understand modules with detailed explanations and examples.
Structured Learning Path: The well-organized curriculum ensures easy learning experience
Who this course is for:
Beginner ML practitioners eager to learn Deep Learning
Python Developers with basic ML knowledge
Anyone who wants to learn about deep learning based computer vision algorithms
Requirements
MACHINE LEARNING Basics
Python
Last Updated 3/2024
