Python for Deep Learning

The "Python for Deep Learning" provides a comprehensive and hands-on exploration of one of the most exciting and rapidly evolving fields in artificial intelligence (AI). This course is designed to equip participants with the foundational knowledge and practical skills needed to understand, implement, and innovate in the realm of deep learning.

Starting From: 22nd January 2024

Course Duration: 20 Hours

Class Timings: 6:50PM to 9:20PM

Course Fee: ₹20,000/-


About Course


Participants are expected to have a basic understanding of machine learning concepts, linear algebra, and probability. Familiarity with programming languages such as Python and a willingness to engage in hands-on coding exercises is highly recommended

Course Objectives:

  • Foundations of Neural Networks:

    • Introduction to neural networks and their historical context.
    • Basic concepts of perceptrons and activation functions.
    • Understanding the structure and functioning of a neural network.

  • Deep Neural Networks:

    • Delving into the architecture of deep neural networks (DNNs).
    • Exploring the role of hidden layers and the vanishing/exploding gradient problem.
    • Activation functions and their impact on model performance.

  • Convolutional Neural Networks (CNNs):

    • Understanding CNN architecture and its applications in image processing.
    • Feature extraction and hierarchical representation in CNNs.
    • Hands-on exercises on image classification tasks using CNNs.

  • Recurrent Neural Networks (RNNs):

    • Introduction to sequential data processing with RNNs.
    • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.
    • Applications of RNNs in natural language processing and time-series analysis.

  • Optimization Techniques:

    • Gradient descent and its variants (e.g., stochastic gradient descent).
    • Exploring optimization challenges in deep learning.
    • Regularization techniques to prevent overfitting.

  • Transfer Learning and Fine-Tuning:

    • Leveraging pre-trained models for specific tasks.
    • Fine-tuning models to adapt to new datasets or tasks.
    • Practical applications and case studies.

  • Generative Models:

    • Introduction to generative adversarial networks (GANs) and variational autoencoders (VAEs).
    • Creating artificial data using GANs.
    • Applications in image generation, style transfer, and data augmentation.

  • Deep Reinforcement Learning:

    • Basics of reinforcement learning and its integration with deep learning.
    • Q-learning, policy gradients, and deep Q-networks.
    • Applications in game playing, robotics, and decision-making.

  • Ethical Considerations and Future Trends:

    • Discussing ethical considerations in deep learning applications.
    • Exploring emerging trends in the field.
    • The societal impact and responsible use of deep learning.

Course Format:

The "Python for Deep Learning" course employs a combination of lectures, hands-on coding exercises, and projects to reinforce learning. Participants are encouraged to actively engage with the material and seek assistance through Q&A sessions and forums.

Upon completion of the Deep Learning course, participants will possess the knowledge and skills to tackle real-world problems using deep learning techniques. They will be equipped to design, implement, and evaluate deep learning models, making them valuable contributors to the rapidly evolving field of artificial intelligence.


Earn a Certificate upon completion

Online Classes

Start instantly and learn at your own

Life Time Accessibility

Set and maintain flexible deadlines.

Beginner Level

No prior experience required.

Program Curriculum
Deep Learning Introduction
  • Recommender Systems
  • RS in Python
  • RS Project
  • Natural Language Processing
  • NLP in Python
  • Deep Learning Introduction
  • Introduction to ANN
Deep Learning Advanced
  • Installing Tensorflow
  • Perceptron Model
  • Neural Networks
  • Activation Functions
  • Multi Class Classification Consideration
  • Cost Functions and Gradient Descent
  • Backpropagation
  • TensorFlow vs Keras

Our Program Focuses On Three Key Areas

Number 1


Number 2


Number 3



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