Udemy - Python for Deep Learning - Build Neural Networks in Python

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[ DevCourseWeb.com ] Udemy - Python for Deep Learning - Build Neural Networks in Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction to Deep Learning
    • 1. What is a Deep Learning .mp4 (11.6 MB)
    • 1. What is a Deep Learning .srt (3.9 KB)
    • 2. Why is Deep Learning Important.mp4 (7.2 MB)
    • 2. Why is Deep Learning Important.srt (2.1 KB)
    • 3. Software and Frameworks.mp4 (5.4 MB)
    • 3. Software and Frameworks.srt (0.9 KB)
    10. Implementation of CNN in Python
    • 1. Dataset.mp4 (6.2 MB)
    • 1. Dataset.srt (1.0 KB)
    • 2. Importing libraries.mp4 (11.1 MB)
    • 2. Importing libraries.srt (2.6 KB)
    • 3. Building the CNN model.mp4 (47.6 MB)
    • 3. Building the CNN model.srt (11.4 KB)
    • 4. Accuracy of the model.mp4 (8.8 MB)
    • 4. Accuracy of the model.srt (0.8 KB)
    2. Artificial Neural Networks (ANN)
    • 1. Introduction.mp4 (8.9 MB)
    • 1. Introduction.srt (1.4 KB)
    • 2. Anatomy and function of neurons.mp4 (7.2 MB)
    • 2. Anatomy and function of neurons.srt (1.4 KB)
    • 3. An introduction to the neural network.mp4 (11.5 MB)
    • 3. An introduction to the neural network.srt (3.5 KB)
    • 4. Architecture of a neural network.mp4 (9.1 MB)
    • 4. Architecture of a neural network.srt (1.7 KB)
    3. Propagation of information in ANNs
    • 1. Feed-forward and Back Propagation Networks.mp4 (5.8 MB)
    • 1. Feed-forward and Back Propagation Networks.srt (1.2 KB)
    • 2. Backpropagation In Neural Networks.mp4 (5.4 MB)
    • 2. Backpropagation In Neural Networks.srt (0.9 KB)
    • 3. Minimizing the cost function using backpropagation.mp4 (5.0 MB)
    • 3. Minimizing the cost function using backpropagation.srt (1.6 KB)
    4. Neural Network Architectures
    • 1. Single layer perceptron (SLP) model.mp4 (4.8 MB)
    • 1. Single layer perceptron (SLP) model.srt (1.1 KB)
    • 2. Radial Basis Network (RBN).mp4 (4.4 MB)
    • 2. Radial Basis Network (RBN).srt (0.9 KB)
    • 3. Multi-layer perceptron (MLP) Neural Network.mp4 (4.7 MB)
    • 3. Multi-layer perceptron (MLP) Neural Network.srt (0.8 KB)
    • 4. Recurrent neural network (RNN).mp4 (6.0 MB)
    • 4. Recurrent neural network (RNN).srt (1.3 KB)
    • 5. Long Short-Term Memory (LSTM) networks.mp4 (6.5 MB)
    • 5. Long Short-Term Memory (LSTM) networks.srt (1.5 KB)
    • 6. Hopfield neural network.mp4 (5.3 MB)
    • 6. Hopfield neural network.srt (1.3 KB)
    • 7. Boltzmann Machine Neural Network.mp4 (4.7 MB)
    • 7. Boltzmann Machine Neural Network.srt (0.9 KB)
    5. Activation Functions
    • 1. What is the Activation Function.mp4 (8.6 MB)
    • 1. What is the Activation Function.srt (1.9 KB)
    • 2. Important Terminologies.mp4 (4.6 MB)
    • 2. Important Terminologies.srt (0.7 KB)
    • 3. The sigmoid function.mp4 (7.1 MB)
    • 3. The sigmoid function.srt (2.3 KB)
    • 4. Hyperbolic tangent function.mp4 (6.3 MB)
    • 4. Hyperbolic tangent function.srt (1.3 KB)
    • 5. Softmax function.mp4 (4.2 MB)
    • 5. Softmax function.srt (0.9 KB)
    • 6. Rectified Linear Unit (ReLU) function.mp4 (5.3 MB)
    • 6. Rectified Linear Unit (ReLU) function.srt (1.5 KB)
    • 7. Leaky Rectified Linear Unit function.mp4 (4.0 MB)
    • 7. Leaky Rectified Linear Unit function.srt (0.9 KB)
    6. Gradient Descent Algorithm
    • 1. What is Gradient Decent.mp4 (9.4 MB)
    • 1. What is Gradient Decent.srt (2.1 KB)
    • 2. What is Stochastic Gradient Decent.mp4 (6.0 MB)
    • 2. What is Stochastic Gradient Decent.srt (2.0 KB)
    • 3. Gradient Decent vs Stochastic Gradient Decent.mp4 (6.2 MB)
    • 3. Gradient Decent vs Stochastic Gradient Decent.srt (0.8 KB)
    7. Summary Overview of Neural Networks
    • 1. How artificial neural networks work.mp4 (23.2 MB)
    • 1. How artificial neural networks work.srt (3.8 KB)
    • 2. Advantages of Neural Networks.mp4 (4.2 MB)
    • 2. Advantages of Neural Networks.srt (1.2 KB)
    • 3. Disadvantages of Neural Networks.mp4 (3.4 MB)
    • 3. Disadvantages of Neural Networks.srt (0.8 KB)
    • 4. Applications of Neural Networks.mp4 (6.4 MB)
    • 4. Applications of Neural Networks.srt (2.1 KB)
    8. Implementation of ANN in Python
    • 1. Introduction.mp4 (4.7 MB)
    • 1. Introduction.srt (0.6 KB)
    • 10. Feature scaling.mp4 (23.4 MB)
    • 10. Feature scaling.srt (3.9 KB)
    • 11. Building the Artificial Neural Network.mp4 (15.9 MB)
    • 11. Building the Artificial Neural Network.srt (1.9 KB)
    • 12. Adding the input layer and the first hidden layer.mp4 (23.5 MB)
    • 12. Adding the input layer and the first hidden layer.srt (3.2 KB)
    • 13. Adding the next hidden layer.mp4 (11.2 MB)
    • 13. Adding the next hidden layer.srt (1.3 KB)
    • 14. Adding the output layer.mp4 (12.2 MB)
    • 14. Adding the output layer.srt (1.6 KB)
    • 15. Compiling the artificial neural network.mp4 (19.6 MB)
    • 15. Compiling the artificial neural network.srt (3.0 KB)
    • 16. Fitting the ANN model to the training set.mp4 (22.4 MB)
    • 16. Fitting the ANN model to the training set.srt (2.3 KB)
    • 17. Predicting the test set results.mp4 (25.9 MB)
    • 17. Predicting the test set results.srt (4.7 KB)
    • 2. Exploring the dataset.mp4 (11.5 MB)
    • 2. Exploring the dataset.srt (1.3 KB)
    • 3. Problem Statement.mp4 (3.2 MB)
    • 3. Problem Statement.srt (0.8 KB)
    • 4. Data Pre-processing.mp4 (13.7 MB)
    • 4. Data Pre-processing.srt (4.0 KB)
    • 5. Loading the dataset.mp4 (9.2 MB)
    • 5. Loading the dataset.srt (1.2 KB)
    • 6. Splitting the dataset into independent and dependent variables.mp4 (22.8 MB)
    • 6. Splitting the dataset into independent and dependent variables.srt (3.2 KB)
    • 7. Label encoding using scikit-learn.mp4 (28.0 MB)
    • 7. Label encoding using scikit-learn.srt (4.5

Description

Python for Deep Learning: Build Neural Networks in Python



https://DevCourseWeb.com

Instructors: Meta Brains
10 sections • 58 lectures • 2h 4m
Video: MP4 1280x720 44 KHz | English + Sub
Updated 1/2022 | Size: 656 MB

Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks

What you'll learn
Learn the fundamentals of the Deep Learning theory
Learn how to use Deep Learning in Python
Learn how to 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

Requirements
Experience with the basics of coding in Python
Basic mathematical skills
Readiness, flexibility, and passion for learning
Description
Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence tasks.



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seeders:9
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Udemy - Python for Deep Learning - Build Neural Networks in Python
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