Generate a sample dataset using Scikit-Learn.
Implement an activation function and feed-forward propagation in a multi-layer ANN in Python code
Utilize gradient descent to adjust the weights of each layer of our ANN through back-propagation implementation in Python code
In this 1-hour long project-based course, you will learn basic principles of how Artificial Neural Networks (ANNs) work, and how this can be implemented in Python. Together, we will explore basic Python implementations of feed-forward propagation, back propagation using gradient descent, sigmoidal activation functions, and epoch training, all in the context of building a basic ANN from scratch. All of this will be done on Ubuntu Linux, but can be accomplished using any Python I.D.E. on any operating system. We will be using the IDLE development environment to write a single script to code our simple ANN. We will avoid using advanced frameworks such as Tensorflow or Pytorch, for educational purposes. Note that the resulting ANN we build will be use-case agnostic and be provided with dummy inputs. Hence, while the ANN we build and train today may appear to be a useless demonstration, it can easily be adapted to any type of use case if given proper, meaningful inputs. I would encourage learners to experiment- How easy is it to add more layers without using frameworks like Tensorflow? What if we add more nodes? What limitations do we come across? The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:
Generate a dataset using Scikit-Learn
Plot generated sample dataset to a graph using pyplot
For each layer, multiply inputs by randomly generated weights
For each layer, calculate the dot products of our two-dimensional sample features
Write a sigmoidal activation function in Python and pass the dot product of our features through it before passing as input to the next layer to accomplish feed-forward propagation
Write a cost function in Python based on the Mean Squared Error method
Utilize gradient descent to adjust the weights of each layer of our ANN through back-propagation implementation in Python code
您的工作空間就是瀏覽器中的雲桌面,無需下載
在分屏視頻中,您的授課教師會為您提供分步指導
It is an excellent project and is an amazing way of testing the old concepts and applying them in a new way.
The Virtual machine reloads quite often which gets very irritating, the project is great besides that.
如果我購買指導項目,會得到什麼?
購買指導項目後,您將獲得完成指導項目所需的一切,包括通過Web 瀏覽器訪問云桌面工作空間,工作空間中包含您需要了解的文件和軟件,以及特定領域的專家提供的分步視頻說明。
指導項目可在台式設備和移動設備上學習嗎?
由於您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面,因此指導項目不在移動設備上提供。
指導項目的講師是誰?
指導項目講師是特定領域的專家,他們在項目的技能、工具或領域方面經驗豐富,並且熱衷於分享自己的知識以影響全球數百萬的學生。
我能在完成指導項目後從中下載作品嗎?
您可以從指導項目中下載並保留您創建的任何文件。為此,您可以在訪問云桌面時使用‘文件瀏覽器’功能。
我能夠退款嗎?退款政策是如何規定的?
指導項目不符合退款條件。 請查看我們完整的退款政策。
有助學金嗎?
指導項目不提供助學金。
我能旁聽指導項目並免費觀看視頻部分嗎?
指導項目不支持旁聽。
我需要具備多少經驗才能做這個指導項目?
您可在頁面頂部點按此指導項目的經驗級別,查看任何知識先決條件。對於指導項目的每個級別,您的講師會逐步為您提供指導。
我能直接通過 Web 瀏覽器來完成此指導項目,而不必安裝特殊軟件嗎?
是,您可以在瀏覽器的雲桌面中獲得完成指導項目所需的一切。
指導項目的學習體驗如何?
您可以直接在瀏覽器中於分屏環境下完成任務,以此從做中學。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有講師逐步指導您完成項目。
還有其他問題嗎?請訪問 學生幫助中心。