Deep Learning

Online | Self-paced | Start Anytime
Core
Early Access

About the Course

Take your first step into the world of AI with our Deep Learning course. This comprehensive introduction covers common deep learning models, with a focus on PyTorch. Through real-life examples, you’ll explore the practical applications of AI and deep learning models. By the end of the course, you’ll be able to implement a complete end-to-end model, giving you a strong foundation for your AI journey.

Curriculum

  • Module 0: Getting Started
    DESCRIPTION

    Overview:

    This module shows the basic setups and intro into deep learning concepts

    Tools to Cover:

    • Intro to AI Fundamentals
    • Getting Started Guide

  • Module 1: Deep Neural Network
    DESCRIPTION

    Overview:

    This module examines the basics of deep neural networks, including their architecture and how they simulate human brain functions for learning patterns.

    Tools to Cover:

    • Neural Networks
    • PyTorch

  • Module 2: Deep Learning Tools & Frameworks
    DESCRIPTION

    Overview:

    This module dives into deep learning tools and architectures, exploring how deep neural networks can be used for complex data analysis tasks.

    Tools to Cover:

    • Torch Lighting
    • Tensorflow & Kera
    • Jax and Flax

  • Module 3: Use Cases
    DESCRIPTION

    Overview:

    This module applies the knowledge of deep learning concepts into real-life applications

    Tools to Cover:

    • Text Classification using PyTorch
    • Image Classification using PyTorch
    • RNN
    • LSTM
    • Graph NN

Learning Outcomes

By the end of this course, participants will be able to:

  • Explain the core concepts of artificial intelligence and deep learning, including the structure and function of neural networks.
  • Design and implement deep neural networks using PyTorch, understanding how they model complex patterns in data
  • Utilize tools like Torch Lightning, TensorFlow, Keras, Jax, and Flax to develop, train, and optimize deep learning models for various tasks.
  • Implement deep learning models for tasks like text classification, image classification, and time series analysis using RNNs and LSTMs
  • Apply graph neural networks (GNNs) to analyze data that exists in non-Euclidean space, such as social networks and molecular structures.

Tools

Jupyter Notebook
Python
Early Access Available!

Get early access to this course at a special rate. Not all content is ready yet, but you can sign up today and unlock new materials as they are released!

Deep Learning
Original price was: $499.00.Current price is: $250.00.
what you will get
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FAQ

Frequently asked questions about the bootcamp

The course is structured into weekly modules, each containing video lectures, reading materials, assignments, and quizzes. You can complete the modules at your own pace, but we recommend following the weekly schedule to stay on track.

You can get support in multiple ways:

  • TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.
  • Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

TAs are available on Slack from 9 AM to 6 PM (ET) Monday to Friday. Outside these hours, you can still post your questions, and TAs will respond as soon as they are back online.

After enrolling in the course, you will receive an invitation link to join the Discord community. Follow the link to create an account or log in to your existing account.

The Discord community offers peer-to-peer support, where you can discuss course topics, share resources, collaborate on projects, and network with fellow learners

The optional mentoring service includes one-on-one sessions with an experienced mentor who can provide personalized guidance, feedback on your progress, and help you set and achieve your learning goals.

Please talk to our Program Advisors to sign up for Mentorship services for an additional cost

Yes, you will have lifetime access to the course materials, including any updates made to the content in the future.

We accept all major credit cards, PayPal, and bank transfers. You can choose your preferred payment method at checkout

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