В любой момент
5 месяцев
Стоимость курса
49 USD/мес.
Подробности и регистрация

Deep Learning

Навыки, которые вы получите:
Deep learning Neural network architecture Mathematical optimization Recurrent neural network Python

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

Necessary preparation

  • Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures.
  • A basic grasp of linear algebra & ML.

The Program

  1. Neural networks and deep learning.
  2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization.
  3. Structuring Machine Learning Projects.
  4. Convolutional Neural Networks.
  5. Sequence models. 

What will you learn

  • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications.
  • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data.
  • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow.
  • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering.
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