Deep Learning Specialization

Deep Learning Specialization
Deep Learning Specialization

What you will learn from this course

  • Build & train deep neural networks, identify key architecture parameters, & implement efficient neural networks & deep learning to your applications
  • Train test sets & analyze variance for DL applications, use standard techniques & optimization algorithms, & build neural networks in TensorFlow
  • Build a CNN & apply it to detection & recognition tasks, use neural style transfer to generate art, & apply algorithms to image, video, & 2D/3D data
  • Build & train RNNs, work with NLP & Word Embeddings, & use HuggingFace tokenizers & transformer models to perform tasks like NER & Question Answering
  • Learn to set up a machine learning problem with a neural network mindset.
  • Learn to use vectorization to speed up your models.
  • Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
  • Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
  • Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
  • Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
  • Learn about recurrent neural networks

By the end of the course, you’ll be able to

  • Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications
  • Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow
  • Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning
  • Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data
  • Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering

Can I download Deep Learning Specialization course?

You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.
Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Deep Learning Specialization course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.
Are there any other coupons available for this course?
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