Deep Learning

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    Course Overview

    Artificial Intelligence (AI) relates to incorporating human intelligence into machines based on algorithms. It is the driving force behind advancing technologies and encompasses machine learning, where machines are programmed to learn by experience. Deep learning, a subset of machine learning is driving all innovations in AI. Deep learning involves repeated execution of algorithms for a task, and every time tweaks it for improved performances. Being the most sought-after skills, AI and Deep Learning training courses help you build and apply convolutional neural networks for image generation and classification, deploy models, predict time-series and much more.

    Technofine24 is a prominent provider of comprehensive Artificial Intelligence and Deep Learning certification training course that empowers you with in-depth knowledge on neural networks, logistic regression, vectorization and provide hands-on experience on real-world applications of deep learning.

    Become an expert in neural networks with our AI and Deep learning training course at Jagyan Academy. Specifically crafted for AI beginners and experienced professionals, we offer a range of customized training in different learning modes, to best-fit your schedules and conveniences.

    COURSE CONTENT

    Introduction to Multi-Layer Perceptron / ANN

    1. Black box techniques
    2. Intution of neural networks
    3. Perceptron algorithm
    4. Calculation of new weights
    5. Non linear boundaries in MLP
    6. Integration function
    7. Activation function
    8. Error surface
    9. Gradient descent algorithm

    Text Mining and Natural Language Processing

    1. Text data generating sources
    2. How to give structure to text structure using bag of words
    3. Terminology used in text data analysis
    4. DTM & TDM
    5. TFIDF & its usage
    6. Word cloud and its interpretation

    Deep Learning Black Box Technique - CNN, RNN

    1. Imagenet classification challenges
    2. Convolution network applications
    3. Challenges in classifying the images using MLP
    4. Parameter explosion
    5. Pooling layers
    6. Fully connected layers
    7. Alexnet case study
    8. Modelling sequence data
    9. Vanishing/Gradient descent explode
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