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Kaggle

Intro to Deep Learning

via Kaggle

Overview

Use TensorFlow and Keras to build and train neural networks for structured data.
  • Learn about linear units, the building blocks of deep learning.
  • Add hidden layers to your network to uncover complex relationships.
  • Use Keras and Tensorflow to train your first neural network.
  • Improve performance with extra capacity or early stopping.
  • Add these special layers to prevent overfitting and stabilize training.
  • Apply deep learning to another common task.
  • Get started with Tensor Processing Units (TPUs)!

Syllabus

  • A Single Neuron
  • Deep Neural Networks
  • Stochastic Gradient Descent
  • Overfitting and Underfitting
  • Dropout and Batch Normalization
  • Binary Classification
  • Detecting the Higgs Boson With TPUs

Taught by

Ryan Holbrook

Reviews

5.0 rating, based on 3 Class Central reviews

Start your review of Intro to Deep Learning

  • The "Introduction to Deep Learning" course on Kaggle offers a well-structured curriculum, blending theory with hands-on exercises. Instructors provide clear explanations, making complex topics easily understandable. Real-world examples and case studies enrich the learning experience, showcasing practical applications across domains. The interactive Kaggle Kernels platform allows collaborative learning and experimentation with datasets and pre-built kernels. Overall, the course exceeds expectations, providing a solid foundation in deep learning fundamentals and practical skills for real-world problem-solving. Whether a beginner or with some experience, I highly recommend this course for anyone venturing into deep learning.
  • Profile image for Jaiesh D
    Jaiesh D
    This course is an excellent starting point for anyone looking to begin a career in deep learning. It covers all the fundamental concepts from the ground up and includes modules where you'll get hands-on experience building your own models.
  • SWAVAGYASHREE
    This course provided a fantastic foundation in neural networks, including:

    Understanding neurons (single & multi-layer)
    Coding with TensorFlow & Keras
    Activation functions (ReLU, ELU, etc.) & layers
    Exploring linear regression & training processes
    Recognizing & preventing overfitting/underfitting
    I dove deep into:

    Loss functions (MAE, MSE, Huber) & optimizers (SGD, Adam)
    Regularization techniques (dropout, batch normalization)
    Binary classification & metrics (accuracy, cross-entropy)
    This practical course even included step-by-step coding using TensorFlow and Keras, making the concepts stick.

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