Deep Learning 1
5.0 - - 2 reviews

Photo Deep Learning 1 course - 2018


You'll be able to :

  • Understand and implement state-of-the-art deep learning algorithms;
  • Use TensorFlow in realistic projects;
  • Understand the limitations of deep learning.

  • Requirements :

  • Intermediate programming skills in Python, and experience with Numpy and Pandas;
  • Have general knowledge about linear algebra and differential calculus;
  • Laptop with minimal computing power: RAM>4GB, GPU.

  • Course features

    Lecturer     Fabio Capela
    Starting     18/March/2019
    Ending     04/May/2019
    Training Sessions     Tuesdays and Thursdays, 6.30-8.30pm
    Full-Immersion Saturdays     06 April and 27 April, 10am-7pm
    Location      Coworking Voisins , Geneva
    # hours     40
    Max Students     15
    Language     English
    Full Price     1600 CHF

    Description

    With this course, you will be able to implement state-of-the-art techniques in deep learning, such as convolutional neural networks, recurrent neural networks, and transfer learning. The course will gradually bring you from the simple perceptron model to powerful algorithms, such as the LSTM models, which are used every day in our phones for voice recognition.

    The course includes two full-immersion Saturdays of 8 hours. During that day, you will be able to implement in TensorFlow a realistic project in sectors such as healthcare, engineering or financial trading.

    This course is tailored to professionals who have already had a first experience in data analysis and that would like to learn to use advanced machine learning tools. It can also be suitable for academic researchers who are looking for career opportunities in the private sector.

    How it works
    How to works
    Registration
    You can pay immediately if you think you have the right level for the course. You will receive a confirmation message. If you are not sure, you can ask for an assessment. We will meet you and discuss with you what are your goals and what is your level. At the end of the assessment, we will tell you what we think is the right choice for you.
    How to works
    Connect with amazing peers
    During the course, you will interact with students coming from the industry and from academia. Coding together and making small hackathons is the best way to learn.
    How to works
    Certificate
    After completion of a project that you will show to experts in the industry, we will provide you with a certificate of completion that shows that you commited until the very end.

    Photo Deep Learning 1 course - 2018




    Reviews
    Deep Learning 1 - December 2018
    Deep learning is used more and more in theoretical particle physics, my main area of expertise. I enrolled in this course with the goal to be able to follow and contribute to the literature. The course provided a solid theoretical foundation of the subject, as well as, an immense number of practical example, which I could straightforwardly apply to my research.

    Following the completion of the course, I have no difficulties understanding the ML seminars given at CERN, and I started using these techniques in my collider projects. Therefore, I am delighted to give the highest possible recommendation for this course. This positive experience already enriched my academic career. Thanks to SamurAI.
    Deep Learning 1 - December 2018
    I was happy to attend the course on Deep Learning offered by Samurai. The most important DL models are covered with a focus on model code implementation examples. The contact with the lecturer is permanent for any questions raised.

    To get full benefits from this course, expect to invest a fair effort in learning as well as in the immersion project. Many thanks to Fabio and the team for rolling-out this program.
    FAQs
    Deep learning has many applications in the real world. Among them, there is text translation, speech recognition, and computer vision, to cite a few. One particular application that has the power to reshape our future is cancer diagnosis through the help of deep convolutional neural networks when applied to medical images.
    We, typically, don't accept people for only a few modules, as we think that a full grasp of the course is necessary to be able to understand which techniques to apply in the real world. Moreover, by only taking a selective number of modules, we will not be able to deliver you the certificate at the end.
    Deep learning techniques rely heavily on what is called "tensorial calculations" that can be parallelized through a GPU. It will therefore be advisable to bring a laptop with a GPU.
    This course is a unique opportunity to understand what people exactly mean by deep learning and how it is applied in fields such as healthcare, autonomous transportation, robotics or financial trading. If you are interested in such topics and advanced analytics, then please see the enrollment prerequisites.

    Photo Deep Learning 1 course - 2018