Deep Learning Course

Embark on a groundbreaking journey in deep learning with our comprehensive course. Dive into cutting-edge concepts such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Master essential skills in Python programming, TensorFlow, and Keras. Our hands-on approach ensures you gain practical experience in building and deploying deep learning models. Join us to unlock the potential of artificial intelligence and revolutionize industries.

|| What will I learn?

  • Understand the fundamental principles of machine learning and its applications.
  • Implement machine learning algorithms using programming languages such as Python.
  • Utilize advanced machine learning methods including ensemble techniques and neural networks.
  • Perform data preprocessing and feature engineering.
  • Evaluate and validate machine learning models using appropriate metrics.
  • Apply supervised and unsupervised learning techniques to solve various problems.

|| What will I learn?

  • Understand the fundamental principles of machine learning and its applications.
  • Implement machine learning algorithms using programming languages such as Python.
  • Utilize advanced machine learning methods including ensemble techniques and neural networks.
  • Perform data preprocessing and feature engineering.
  • Evaluate and validate machine learning models using appropriate metrics.
  • Apply supervised and unsupervised learning techniques to solve various problems.

|| Requirements

  • Basic programming knowledge (preferably in Python)
  • Understanding of fundamental statistical concepts
  • Basic knowledge of linear algebra and calculus

|| Requirements

  • Basic programming knowledge (preferably in Python)
  • Understanding of fundamental statistical concepts
  • Basic knowledge of linear algebra and calculus

    Artificial Neural Network (ANN)

    • Biological and Artificial Neurons
    • Activation Functions
    • Perceptron
    • Feed Forward Network
    • Multilayer Perceptron (MLP)
    • Back Propagation, Deep ANN
    • Optimisation Algorithms
    • Gradient Descent
    • Stochastic Gradient Descent (SGD)
    • MiniBatch Stochastic Gradient Descent
    • Stochastic Gradient Descent with Momentum
    • AdaGrad, RMSProp , Adam
    • Batch Normalisation


    • KERAS
    • What is Keras?
    • How to Install Keras?
    • Why to Use Keras?
    • Different Models of Keras
    • Preprocessing Methods
    • What are the Layers in Keras?


    • Tensorflow 2.0
    • TensorFlow in Realtime Applications
    • Advantages of TensorFlow
    • How to Install TensorFlow
    • TensorFlow 1x vs TensorFlow 2.0
    • Eager Execution in TensorFlow 2.0


    • Convolutional Neural Network(CNN)
    • Introduction to Computer Vision
    • Convolutional Neural Network
    • Architecture of Convolutional network
    • Image as a Matrix, Convolutional Layer
    • Feature Detector & Feature Maps
    • Pooling Layer, Max Pooling
    • Min Pooling, 
    • Avg Pooling
    • Flattening Layer, Padding, Striding
    • Image Augmentation
    • Basics of Digital Images


    • Recurrent Neural Network (RNN)
    • RNN Network Structure
    • Different Types of RNNs
    • Bidirectional RNN
    • Limitations of RNN

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|| Frequently asked question

A Deep Learning Course focuses on teaching participants the theory, algorithms, and applications of deep learning, a subset of machine learning that deals with neural networks comprising multiple layers. It covers topics such as artificial neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their applications in various domains.

This course is suitable for individuals interested in pursuing a career in deep learning, artificial intelligence, or related fields. It caters to beginners with some background in machine learning as well as professionals looking to specialize in deep learning techniques and applications.

Prerequisites may include a solid understanding of machine learning concepts and algorithms, proficiency in a programming language such as Python, and familiarity with linear algebra, calculus, and probability theory. Some courses may also require prior experience with neural networks or deep learning frameworks.

Most reputable Deep Learning courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Some courses may offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities. However, this varies depending on the course provider.

After completing the course, you may continue to have access to resources such as course materials, coding exercises, alumni networks, coding communities, and additional learning resources. Some providers offer lifetime access to course materials or alumni benefits to support your continued growth and success.

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