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Deep Learning & Neural Networks Python – Keras

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Ever dreamt of navigating the intricate pathways of the human brain’s counterparts in the machine world? Welcome to the “Deep Learning & Neural Networks Python – Keras” course, an odyssey that paints a vivid picture of the intertwining webs of neural networks. Our meticulously crafted modules transport you from the foundational stones of Python basics straight into the high towers of Theano and TensorFlow library installations.

Transition smoothly from understanding the complex labyrinths of neural structures to implementing them with flair. Whether it’s the Pima Indian Diabetes or the Iris Flower Multi-Class models, we’ll unravel the steps, terminologies, and techniques with precision. Dive deeper into the shimmering waters of Convolutional Neural Networks, where you’ll gain first-hand knowledge on data giants like the MNIST Handwritten Digit Recognition and the CIFAR-10 Object Recognition Datasets.

As the cherry on top, our Deep Learning & Neural Networks Python course does not merely offer theoretical insights. Instead, we illuminate the path to applying these neural structures in tangible scenarios. From checkpointing neural network improvements to plotting model behavior history and understanding image augmentation, every corner you turn unveils another layer of deep learning wonder. Join us in this transformative journey and sculpt your future in the AI realm.

course-benefits Learning outcome

  • Differentiate between machine learning and deep learning in AI projects.
  • Navigate through Python basics and library installations including Theano and TensorFlow.
  • Develop and evaluate models including Pima Indian Diabetes, Iris Flower Multi-Class, and more.
  • Gain proficiency in convolutional neural networks with insights on MNIST and CIFAR-10 Datasets.
  • Apply knowledge in practical scenarios by loading and predicting various models.
  • Understand the importance and application of dropout regularization and learning rate schedules.
  • Effectively use checkpointing to improve and save neural network models.

course-why Why should I take this course?

  • Comprehensive coverage of deep learning and neural networks.
  • Step-by-step guide on developing and evaluating various models.
  • Access to real datasets like MNIST and CIFAR-10.
  • Insights on advanced concepts like dropout regularization and learning rate schedules.
  • Learn how to use checkpointing to improve neural network models.

course-why Career Path

  • Data Scientist.
  • Machine Learning Engineer.
  • AI Research Scientist.
  • Neural Network Developer.
  • Deep Learning Specialist.
  • Computer Vision Engineer.

course-requirement Requirements

  • Basic understanding of Python programming.
  • Basic knowledge of machine learning concepts.
  • Willingness to learn and apply new skills in deep learning.

Course Curriculum

  • play Course Introduction and Table of Contents
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