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Deep Learning Essentials

Designed for ambitious professionals seeking to master the foundations of deep learning, this program blends essential neural concepts, practical coding, and real-world applications to equip participants with the skills to build and apply advanced AI models in today’s evolving landscape.

 

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    What to Expect From This Deep Learning Essentials Course?

    By the end, participants will be able to:

    • Understand the structure and function of neural networks.
    • Apply activation functions, layers, and loss functions effectively.
    • Utilize TensorFlow and Keras for building AI models.
    • Develop Convolutional Neural Networks (CNNs) for image classification tasks.
    • Explore Recurrent Neural Networks (RNNs) for sequence-based data.
    • Integrate deep learning models into practical business and technical solutions.

    Who Should Enroll in this Deep Learning Essentials Course?

    • Data scientists and AI enthusiasts are aiming to deepen their technical expertise.
    • Software engineers and developers seeking to build deep learning applications.
    • Professionals in IT and business looking to apply AI solutions in real-world contexts.
    • Students and researchers interested in advanced machine learning concepts.
    • Anyone aspiring to specialize in neural networks and deep learning.

    What are the acquired skills from this Deep Learning Essentials Course?

    • Designing and training neural networks.
    • Applying activation and loss functions in deep learning models.
    • Building AI solutions using TensorFlow and Keras.
    • Developing CNNs for computer vision tasks.
    • Implementing RNNs for sequential and time-series data.
    • Translating deep learning models into real-world business applications.

    Deep Learning Essentials Self-Study

    Course 

    • Reading Learning Materials. 
    • Pre-Reading file. 
    • Pre and Post Course Assessments. 
    • Modules Exercises. 
    • The language will be English.
    • Module 1: Understanding Neural Networks 
    • Module 2: Activation Functions, Layers, Loss Functions 
    • Module 3: Introduction to TensorFlow & Keras 
    • Module 4: Building CNNs for Image Classification 
    • Module 5: Intro to RNNs for Sequence Data 

    Course Inclusions

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