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[ディープラーニング専門家コースDL1102]ディープラーニングのためのPythonレベル2

Pythonの基礎文法を拡張し、機械学習/ディープラーニングプロジェクトを進めます。

（5.0）受講レビュー 11件

受講生 223名

  • asdfghjkl13551941
Python
Deep Learning(DL)
Anaconda

学習した受講者のレビュー

こんなことが学べます

  • Pythonの基礎

  • ディープラーニング基礎アイテム

  • CNN関連モジュールの動作原理

  • 問題解決能力

mini-projectを自分で解決し、ディープラーニングを学ぶのに必要な実装能力を育ててください！

オリエンテーション映像

[L4DL] Project Currimulum 📑

[全画面リンク]

からLv.1からLv.2まで

[ディープラーニングのためのPythonレベル1]講義でPython文法をもう少し拡張し、ディープラーニングで使用される少し難しいアイテムを直接実装してみます。また、次のように6つのミニプロジェクトを通じて講義を聞くのではなく、実装能力を大きく育てます。

6つのMini-pojects

  1. Top-5 Accuracy
  2. エッジ検出
  3. Convolutional Layer
  4. K-Nearest Neighbor Classification
  5. K-means Clustering

Mini-projectは、単にプログラミング講義を聞くのではなく、実装する能力を育てるために問題の状況を聞いた後、自分で解決してみる時間を先に提供します。以降の説明を聞いた後、もう一度復習する時間を提供します。

プログラミング能力は、自分が考えている内容をどれだけプログラムでうまく作るかによって決まります。このプロジェクトを通じて、 ディープラーニングを学ぶのに必要な 実装能力を練習してください。

Advanced Equations

レベル2コースでは、レベル1よりも少し複雑な式を学びます。そして、これらの式はディープラーニングで活発に使用される式です。

この講義を通して、あなたは次の能力を大幅に向上させることができます。

  • 式を理解する能力
  • 式をプログラムとして実装する能力

次の知識を得ることができます。

  • 今後ディープラーニングで学ぶアイテムの動作原理
  • ベクトル化の必要性

Assembling Building Blocks

どのプログラムも細かく分割すると、その小さなモジュールは基本的な動作で構成されています。

Mini-projectでは、私たちが学んだ小さなモジュールを組み合わせて、 K-nearest neighbor classification、K-means clusteringなどの機械学習アルゴリズム、 convolutional layer、edge detectionなどのディープラーニング関連トピックを直接実装します。

Lecture Materials

  1. このレッスンで扱うすべてのソースコードと簡単な説明をJupyter Notebookファイルとして提供します。

こんな方に
おすすめです

学習対象は
誰でしょう？

  • ディープラーニングを初めて入門される方

  • Pythonを初めて学ぶ方

  • プログラム実施能力が不足している方

  • ディープラーニングとPythonを一緒に始めたい人

  • ディープラーニングの専門コースに参加したい方

前提知識、
必要でしょうか？

  • [ディープラーニングのためのPythonレベル1]受講生

こんにちは
です。

2,989

受講生

124

受講レビュー

81

回答

5.0

講座評価

13

講座

강의 이력

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  • [패스트캠퍼스] 수학적으로 접근하는 딥러닝

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カリキュラム

全体

60件 ∙ (42時間 28分)

講座資料（こうぎしりょう）:

授業資料
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受講レビュー

全体

11件

5.0

11件の受講レビュー

  • alexna9046님의 프로필 이미지
    alexna9046

    受講レビュー 5

    ∙

    平均評価 5.0

    5

    100% 受講後に作成

    I felt that the class was structured in a way that I could learn a lot, and the practical learning was good. Thank you.

    • endymion님의 프로필 이미지
      endymion

      受講レビュー 14

      ∙

      平均評価 5.0

      5

      100% 受講後に作成

      It was a great lecture! I had some basic knowledge of grammar and numpy, but it was very helpful to learn various methods that can be used in relation to deep learning. In particular, I think I was able to understand it more deeply by implementing Data Generation, Convolutional Layer, K-Nearest Neighbor, and K-means Clustering myself. I plan to take follow-up lectures. Thank you for the great lecture!

      • lym9309201853님의 프로필 이미지
        lym9309201853

        受講レビュー 8

        ∙

        平均評価 5.0

        5

        100% 受講後に作成

        Thank you so much, it's really helpful!

        • junyak2님의 프로필 이미지
          junyak2

          受講レビュー 2

          ∙

          平均評価 5.0

          5

          100% 受講後に作成

          It's a really good lecture. I was able to improve my implementation skills a lot by implementing various algorithms with limited grammar.

          • airjoy2460님의 프로필 이미지
            airjoy2460

            受講レビュー 18

            ∙

            平均評価 5.0

            5

            80% 受講後に作成

            Python feels more and more familiar to me.

            公開していない講座のため、受講が制限されます。
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