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Deep Learning

(CTU-AI310.AE1) / ISBN : 979-8-90059-933-5
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1

Foundations of Deep Learning

  • Defining What Deep Learning Means
  • Using Deep Learning in the Real World
  • Considering the Deep Learning Programming Environment
  • Overcoming Deep Learning Hype
  • Defining Machine Learning
  • Considering the Many Different Roads to Learning
  • Pondering the True Uses of Machine Learning
  • Working with Python in this Course
  • Obtaining Your Copy of Anaconda
  • Downloading the Datasets and Example Code
  • Creating the Application
  • Understanding the Use of Indentation
  • Adding Comments
  • Getting Help with the Python Language
  • Working in the Cloud
2

Deep Learning Architectures

  • Understanding Neural Networks
  • Looking Under the Hood of Neural Networks
  • Seeing Data Everywhere
  • Discovering the Benefits of Additional Data
  • Improving Processing Speed
  • Explaining Deep Learning Differences from Other Forms of AI
  • Finding Even Smarter Solutions
  • Beginning the CNN Tour with Character Recognition
  • Explaining How Convolutions Work
  • Detecting Edges and Shapes from Images
  • Introducing Recurrent Networks
  • Explaining Long Short-Term Memory
  • Using Image Classification Challenges
  • Distinguishing Traffic Signs
3

Applying Deep Learning Models

  • Presenting Frameworks
  • Working with Low-End Frameworks
  • Understanding TensorFlow
  • Revealing the Math You Really Need
  • Understanding Scalar, Vector, and Matrix Operations
  • Interpreting Learning as Optimization
  • Combining Variables
  • Mixing Variable Types
  • Switching to Probabilities
  • Guessing the Right Features
  • Learning One Example at a Time
4

Evaluating Deep Learning Models

  • Making Networks Compete
  • Considering a Growing Field
  • Playing a Game with Neural Networks
  • Explaining Alpha-Go
  • Discovering the Incredible Perceptron
  • Hitting Complexity with Neural Networks
  • Struggling with Overfitting
5

Advanced Applications of Deep Learning

  • Distinguishing Classification Tasks
  • Perceiving Objects in Their Surroundings
  • Overcoming Adversarial Attacks on Deep Learning Applications
  • Compiling Math Expressions Using Theano
  • Augmenting TensorFlow Using Keras
  • Dynamically Computing Graphs with Chainer
  • Creating a MATLAB-Like Environment with Torch
  • Performing Tasks Dynamically with PyTorch
  • Accelerating Deep Learning Research Using CUDA
  • Supporting Business Needs with Deeplearning4j
  • Mining Data Using Neural Designer
  • Training Algorithms Using Microsoft Cognitive Toolkit (CNTK)
  • Exploiting Full GPU Capability Using MXNet

1

Foundations of Deep Learning

  • Exploring Jupyter Notebook
  • Understanding Cells of Jupyter Notebook
  • Understanding Indentation and Adding Comments in a Notebook
2

Deep Learning Architectures

  • Creating a Neural Network Model
  • Building a LeNet5 Network
  • Creating an Image Classifier Using CNNs
3

Applying Deep Learning Models

  • Working with Matrices
  • Analyzing Data Using Linear Regression
  • Using Polynomial Expansion to Model Complex Relations
  • Analyzing Data Using Logistic Regression

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