Python for Deep Learning

Python for Deep Learning

by Najib Abdallah
Released September 2022
Publisher(s): Manning Publications
ISBN: 10000DIVC2022155

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Video description

If you know the basics of Python and you want to know deep learning, this course is designed for you. You’ll learn the theory behind this branch of artificial intelligence and machine learning, as well as the practical skills of building neural networks to create deep learning models for prediction and for automating and simplifying tasks.

Once you’ve digested the fundamentals, we’ll walk you through a project: implementing an artificial neural network in Python to create a deep learning model. Step by step, you’ll see how to work with datasets and build each layer of the network. By the end of the course, you will be familiar with the fundamental neural network architectures, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs)) networks, and you will be able to build your own DL neural networks using Python, Keras, and Tensorflow. In short, you will be ready to build models and create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.

This video course stands out from the hundreds of machine learning resources available on the internet because it filters out the fluff and unnecessary information and focuses on the essentials you need to get started on your deep learning journey. Consider this a fundamentals course that suits both beginners and more advanced deep learning practitioners who are looking to refresh or fill in the gaps in their knowledge.


Distributed by Manning Publications

This course was created independently by Meta Brains and is distributed by Manning through our exclusive liveVideo platform.



About the Technology


About the Video


What's Inside
  • Fundamentals of deep learning theory
  • Using Python for deep learning
  • Use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Build artificial neural networks with Tensorflow and Keras


About the Reader
  • Experience with the basics of coding in Python
  • Basic mathematical skills


About the Author

Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for coding, finance, and Excel.

They bring together both professional and educational experiences to create world-class training programs accessible to everyone.

Currently, They're focused on the next great revolution in computing: the metaverse. The ultimate objective is to train the next generation of talent so we can code and build the metaverse together!



Quotes

Table of contents

  1. Introduction to Deep Learning
    1. What is a Deep Learning?
    2. Why is Deep Learning Important?
    3. Software and Frameworks
  2. Artificial Neural Networks (ANN)
    1. Introduction
    2. Anatomy and function of neurons
    3. An introduction to the neural network
    4. Architecture of a neural network
  3. Propagation of information in ANNs
    1. Feed-forward and Back Propagation Networks
    2. Backpropagation In Neural Networks
    3. Minimizing the cost function using backpropagation
  4. Neural Network Architectures
    1. Single layer perceptron (SLP) model
    2. Radial Basis Network (RBN)
    3. Multi-layer perceptron (MLP) Neural Network
    4. Recurrent neural network (RNN)
    5. Long Short-Term Memory (LSTM) networks
    6. Hopfield neural network
    7. Boltzmann Machine Neural Network
  5. Activation Functions
    1. What is the Activation Function?
    2. Important Terminologies
    3. The sigmoid function
    4. Hyperbolic tangent function
    5. Softmax function
    6. Rectified Linear Unit (ReLU) function
    7. Leaky Rectified Linear Unit function
  6. Gradient Descent Algorithm
    1. What is Gradient Decent?
    2. What is Stochastic Gradient Decent?
    3. Gradient Decent vs Stochastic Gradient Decent
  7. Summary Overview of Neural Networks
    1. How artificial neural networks work?
    2. Advantages of Neural Networks
    3. Disadvantages of Neural Networks
    4. Applications of Neural Networks
  8. Implementation of ANN in Python
    1. Introduction
    2. Exploring the dataset
    3. Problem Statement
    4. Data Pre-processing
    5. Loading the dataset
    6. Splitting the dataset into independent and dependent variables
    7. Label encoding using scikit-learn
    8. One-hot encoding using scikit-learn
    9. Training and Test Sets: Splitting Data
    10. Feature scaling
    11. Building the Artificial Neural Network
    12. Adding the input layer and the first hidden layer
    13. Adding the next hidden layer
    14. Adding the output layer
    15. Compiling the artificial neural network
    16. Fitting the ANN model to the training set
    17. Predicting the test set results
  9. Convolutional Neural Networks (CNN)
    1. Introduction
    2. Components of convolutional neural networks
    3. Convolution Layer
    4. Pooling Layer
    5. Fully connected Layer
  10. Implementation of CNN in Python
    1. Dataset
    2. Importing libraries
    3. Building the CNN model
    4. Accuracy of the model

Product information

  • Title: Python for Deep Learning
  • Author(s): Najib Abdallah
  • Release date: September 2022
  • Publisher(s): Manning Publications
  • ISBN: 10000DIVC2022155