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    2023 Python for Deep Learning and Artificial Intelligence

    Posted By: lucky_aut
    2023 Python for Deep Learning and Artificial Intelligence

    2023 Python for Deep Learning and Artificial Intelligence
    Published 6/2023
    Duration: 17h 5m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 6.39 GB
    Genre: eLearning | Language: English

    Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Auto Encoders, GAN, Transfer Learning, Deploying Deep Learning Models

    What you'll learn
    The basics of Python programming language
    Foundational concepts of deep learning and neural networks
    How to build a neural network from scratch using Python
    Advanced techniques in deep learning using TensorFlow 2.0
    Convolutional neural networks (CNNs) for image classification and object detection
    Recurrent neural networks (RNNs) for sequential data such as time series and natural language processing
    Generative adversarial networks (GANs) for generating new data samples
    Transfer learning in deep learning
    Reinforcement learning and its applications in AI
    Deployment options for deep learning models
    Applications of deep learning in AI, such as computer vision, natural language processing, and speech recognition
    The current and future trends in deep learning and AI, as well as ethical and societal implications.


    Requirements
    Basic understanding of programming concepts and mathematics
    A laptop or a computer with an internet connection
    A willingness to learn and explore the exciting field of deep learning and artificial intelligence
    Description
    This comprehensive course covers the latest advancements in deep learning and artificial intelligence using Python. Designed for both beginner and advanced students, this course teaches you the foundational concepts and practical skills necessary to build and deploy deep learning models.
    Module 1: Introduction to Python and Deep Learning
    Overview of Python programming language
    Introduction to deep learning and neural networks
    Module 2: Neural Network Fundamentals
    Understanding activation functions, loss functions, and optimization techniques
    Overview of supervised and unsupervised learning
    Module 3: Building a Neural Network from Scratch
    Hands-on coding exercise to build a simple neural network from scratch using Python
    Module 4: TensorFlow 2.0 for Deep Learning
    Overview of TensorFlow 2.0 and its features for deep learning
    Hands-on coding exercises to implement deep learning models using TensorFlow
    Module 5: Advanced Neural Network Architectures
    Study of different neural network architectures such as feedforward, recurrent, and convolutional networks
    Hands-on coding exercises to implement advanced neural network models
    Module 6: Convolutional Neural Networks (CNNs)
    Overview of convolutional neural networks and their applications
    Hands-on coding exercises to implement CNNs for image classification and object detection tasks
    Module 7: Recurrent Neural Networks (RNNs)
    [Coming Soon]
    Overview of recurrent neural networks and their applications
    Hands-on coding exercises to implement RNNs for sequential data such as time series and natural language processing
    By the end of this course, you will have a strong understanding of deep learning and its applications in AI, and the ability to build and deploy deep learning models using Python and TensorFlow 2.0. This course will be a valuable asset for anyone looking to pursue a career in AI or simply expand their knowledge in this exciting field.
    Who this course is for:
    Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning.
    Developers and programmers who want to learn how to build and deploy machine learning models in a production environment.
    Researchers and academics who want to understand the latest developments and applications of machine learning.
    Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations.
    Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence.
    Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.


    More Info