Data Science

TensorFlow 2: Deep Learning Specialization

(0 reviews)
Share icon
Coursera With GroupifyAI

The specialization caters to machine learning professionals seeking TensorFlow skills through a structured progression from basics to advanced topics, emphasizing practical application through capstone projects.

Key AI Functions:Tensorflow, keras, Probabilistic Neural Networks, TensorFlow Probability

Description for TensorFlow 2: Deep Learning Specialization

  • Audience: Machine learning researchers and practitioners who are interested in acquiring practical skills in TensorFlow.
  • Course Structure: The specialization consists of three courses, which commence with fundamental concepts and proceed to more advanced topics such as probabilistic approaches and custom model development.
  • Focus Areas: Each course emphasizes distinct components, including fundamental model construction and validation, as well as more sophisticated subjects such as custom layers and probabilistic models that utilize TensorFlow Probability.
  • Applied Learning: Capstone projects and programming assignments that encompass a variety of applications, including image classification and text generation, are used to refine practical skills.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by Imperial College London

    Duration: 3 months at 10 hours a week

    Schedule: Flexible

    Reviews for TensorFlow 2: Deep Learning Specialization

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for TensorFlow 2: Deep Learning Specialization

    Introduces the fundamental procedures for the development, scripting, and training of a machine-learned model in Google Cloud.

    #tensorflow #c++
    Visit icon

    Through hyperparameter tuning, regularization, and TensorFlow application, this course emphasizes the optimization of machine learning models.

    #tensorflow #python programming
    Visit icon

    Using Vertex AI and BigQuery ML, the course instructs students on how to improve data quality, construct AutoML models, and optimize models using performance metrics.

    #tensorflow #python programming
    Visit icon

    Through practical experiments utilizing TensorFlow and Google Cloud Platform, this�course offers a thorough grasp of machine learning, from strategy to deployment.

    #tensorflow #python programming
    Visit icon

    Streamline data analysis and deployment by mastering the integration of machine learning into data pipelines using Google Cloud products such as AutoML, BigQuery ML, and Vertex AI.

    #tensorflow #bigquery
    Visit icon

    Gain proficiency in the development of machine learning models and big data pipelines by utilizing Google Cloud's state-of-the-art tools, such as BigQuery, Dataflow, Vertex AI, and Pub/Sub.

    #tensorflow #bigquery
    Visit icon

    Acquire proficiency in machine learning and deep learning methodologies, such as TensorFlow, CNNs, RNNs, LSTMs, and NLP, to facilitate efficient data analysis.

    #tensorflow #machine learning
    Visit icon

    This introductory course examines machine learning applications in finance, culminating in a capstone project focused on predicting bank closures.

    #tensorflow #financial engineering
    Visit icon

    With an emphasis on CI/CD, cloud architecture, and training workflows, this course covers MLOps technologies and best practices for installing, assessing, and running ML systems on Google Cloud.

    #tensorflow #bigquery
    Visit icon

    In the context of machine learning, this course teaches how to use Vertex AI for monitoring and prediction, manage and preprocess data, and apply model tweaking.

    #tensorflow #machine learning
    Visit icon