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Deep Learning

SILAN Technology powered by’s Deep Learning with TensorFlow certification course has been designed by industry experts and aligned with the latest best practices to transform you into deep learning expert. You’ll learn deep learning concepts in detail and the TensorFlow open source framework, implement deep learning algorithms, build artificial neural networks, and traverse layers of data abstraction to understand the power of data and prepare you for an exciting career in deep learning.

Deep Learning Course Overview

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. This deep learning course with TensorFlow is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.

Deep Learning Course Curriculum

What skills you will learn with this Deep Learning course?
  • Understand the TensorFlow concepts from scratch and its main functions, operations.
  • Implement deep learning algorithms, understand neural networks.
  • Master in convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
  • Build deep learning models in TensorFlow and interpret the results
  • Understand the language and fundamental concepts of artificial neural networks
  • Differentiate between artificial intelligence, machine learning and deep learning.
Course Content
  • TensorFlow
    1. TensorFlow Introduction
    2. TensorFlow basic terminology like constant, placeholder & variable
    3. TensorFlow Functions & Operations
    4. An example with MNIST database of handwritten digits
    5. Regression Example
    6. TensorBoard
    7. Modularity
    8. Keras
  • Artificial Neural Networks
    1. Introduction to Artificial Neural Networks
    2. Perceptron Training Rule
    3. Gradient Descent Rule
  • Deep Learning Applications
    1. Image Processing
    2. Natural Language Processing
    3. Speech Recognition
    4. Video Analytics

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