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    Deep Learning and Neural Networks with Python Zero to Expert

    Posted By: lucky_aut
    Deep Learning and Neural Networks with Python Zero to Expert

    Deep Learning and Neural Networks with Python Zero to Expert
    Published 6/2024
    Duration: 9h27m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 4.63 GB
    Genre: eLearning | Language: English

    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



    Requirements
    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

    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, you’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.

    More Info