| 1 - BONUS Section - Don't Miss Out.html | 921.6 B | ||
| 1 - Dataset.en_US.vtt | 819.2 B | ||
| 1 - Dataset.mp4 | 6.2 MB | ||
| 1 - Feed-forward and Back Propagation Networks.en_US.vtt | 1.1 KB | ||
| 1 - Feed-forward and Back Propagation Networks.mp4 | 5.8 MB | ||
| 1 - How artificial neural networks work.en_US.vtt | 3.4 KB | ||
| 1 - How artificial neural networks work.mp4 | 23.2 MB | ||
| 1 - Introduction.en_US.vtt | 3.8 KB | ||
| 1 - Introduction.mp4 | 21 MB | ||
| 1 - Single layer perceptron (SLP) model.en_US.vtt | 1 KB | ||
| 1 - Single layer perceptron (SLP) model.mp4 | 4.7 MB | ||
| 1 - What is Gradient Decent.en_US.vtt | 1.8 KB | ||
| 1 - What is Gradient Decent.mp4 | 9.4 MB | ||
| 1 - What is a Deep Learning.en_US.vtt | 3.4 KB | ||
| 1 - What is a Deep Learning.mp4 | 11.6 MB | ||
| 1 - What is the Activation Function.en_US.vtt | 1.6 KB | ||
| 1 - What is the Activation Function.mp4 | 8.6 MB | ||
| 10 - Feature scaling.en_US.vtt | 3.4 KB | ||
| 10 - Feature scaling.mp4 | 23.4 MB | ||
| 11 - Building the Artificial Neural Network.en_US.vtt | 1.7 KB | ||
| 11 - Building the Artificial Neural Network.mp4 | 15.9 MB | ||
| 12 - Adding the input layer and the first hidden layer.en_US.vtt | 2.8 KB | ||
| 12 - Adding the input layer and the first hidden layer.mp4 | 23.5 MB | ||
| 13 - Adding the next hidden layer.en_US.vtt | 1.1 KB | ||
| 13 - Adding the next hidden layer.mp4 | 11.2 MB | ||
| 14 - Adding the output layer.en_US.vtt | 1.4 KB | ||
| 14 - Adding the output layer.mp4 | 12.2 MB | ||
| 15 - Compiling the artificial neural network.en_US.vtt | 2.6 KB | ||
| 15 - Compiling the artificial neural network.mp4 | 19.6 MB | ||
| 16 - Fitting the ANN model to the training set.en_US.vtt | 2 KB | ||
| 16 - Fitting the ANN model to the training set.mp4 | 22.4 MB | ||
| 17 - Predicting the test set results.en_US.vtt | 4.1 KB | ||
| 17 - Predicting the test set results.mp4 | 25.9 MB | ||
| 2 - Advantages of Neural Networks.en_US.vtt | 1.1 KB | ||
| 2 - Advantages of Neural Networks.mp4 | 4.2 MB | ||
| 2 - Anatomy and function of neurons.en_US.vtt | 1.3 KB | ||
| 2 - Anatomy and function of neurons.mp4 | 7.2 MB | ||
| 2 - Backpropagation In Neural Networks.en_US.vtt | 819.2 B | ||
| 2 - Backpropagation In Neural Networks.mp4 | 5.4 MB | ||
| 2 - Components of convolutional neural networks.en_US.vtt | 921.6 B | ||
| 2 - Components of convolutional neural networks.mp4 | 5.9 MB | ||
| 2 - Course Materials - ANN_Codes.ipynb | 2.7 MB | ||
| 2 - Course Materials - CNN_Codes.ipynb | 5.2 KB | ||
| 2 - Course Materials - Churn_Modelling.csv | 668.8 KB | ||
| 2 - Course Materials - Course Slides.pdf | 4.3 MB | ||
| 2 - Course Materials - mnist_test.csv | 17.5 MB | ||
| 2 - Course Materials - mnist_train.csv | 104.6 MB | ||
| 2 - Course Materials.html | 102.4 B | ||
| 2 - Exploring the dataset.en_US.vtt | 1.1 KB | ||
| 2 - Exploring the dataset.mp4 | 11.5 MB | ||
| 2 - Important Terminologies.en_US.vtt | 716.8 B | ||
| 2 - Important Terminologies.mp4 | 4.6 MB | ||
| 2 - Importing libraries.en_US.vtt | 2.1 KB | ||
| 2 - Importing libraries.mp4 | 11.1 MB | ||
| 2 - Radial Basis Network (RBN).en_US.vtt | 819.2 B | ||
| 2 - Radial Basis Network (RBN).mp4 | 4.4 MB | ||
| 2 - What is Stochastic Gradient Decent.en_US.vtt | 1.8 KB | ||
| 2 - What is Stochastic Gradient Decent.mp4 | 6 MB | ||
| 3 - An introduction to the neural network.en_US.vtt | 3.1 KB | ||
| 3 - An introduction to the neural network.mp4 | 11.5 MB | ||
| 3 - Building the CNN model.en_US.vtt | 9.7 KB | ||
| 3 - Building the CNN model.mp4 | 47.6 MB | ||
| 3 - Convolution Layer.en_US.vtt | 3.2 KB | ||
| 3 - Convolution Layer.mp4 | 12 MB | ||
| 3 - Disadvantages of Neural Networks.en_US.vtt | 716.8 B | ||
| 3 - Disadvantages of Neural Networks.mp4 | 3.4 MB | ||
| 3 - Gradient Decent vs Stochastic Gradient Decent.en_US.vtt | 716.8 B | ||
| 3 - Gradient Decent vs Stochastic Gradient Decent.mp4 | 6.2 MB | ||
| 3 - Minimizing the cost function using backpropagation.en_US.vtt | 1.4 KB | ||
| 3 - Minimizing the cost function using backpropagation.mp4 | 5 MB | ||
| 3 - Multi-layer perceptron (MLP) Neural Network.en_US.vtt | 716.8 B | ||
| 3 - Multi-layer perceptron (MLP) Neural Network.mp4 | 4.7 MB | ||
| 3 - Problem Statement.en_US.vtt | 716.8 B | ||
| 3 - Problem Statement.mp4 | 3.2 MB | ||
| 3 - The sigmoid function.en_US.vtt | 2 KB | ||
| 3 - The sigmoid function.mp4 | 7.1 MB | ||
| 3 - Why is Deep Learning Important.en_US.vtt | 1.8 KB | ||
| 3 - Why is Deep Learning Important.mp4 | 7.1 MB | ||
| 4 - Accuracy of the model.en_US.vtt | 716.8 B | ||
| 4 - Accuracy of the model.mp4 | 8.8 MB | ||
| 4 - Applications of Neural Networks.en_US.vtt | 1.8 KB | ||
| 4 - Applications of Neural Networks.mp4 | 6.4 MB | ||
| 4 - Architecture of a neural network.en_US.vtt | 1.5 KB | ||
| 4 - Architecture of a neural network.mp4 | 9.1 MB | ||
| 4 - Data Pre-processing.en_US.vtt | 3.5 KB | ||
| 4 - Data Pre-processing.mp4 | 13.7 MB | ||
| 4 - Hyperbolic tangent function.en_US.vtt | 1.2 KB | ||
| 4 - Hyperbolic tangent function.mp4 | 6.3 MB | ||
| 4 - Pooling Layer.en_US.vtt | 1.8 KB | ||
| 4 - Pooling Layer.mp4 | 9.7 MB | ||
| 4 - Recurrent neural network (RNN).en_US.vtt | 1.1 KB | ||
| 4 - Recurrent neural network (RNN).mp4 | 6 MB | ||
| 4 - Software and Frameworks.en_US.vtt | 819.2 B | ||
| 4 - Software and Frameworks.mp4 | 5.4 MB | ||
| 5 - Fully connected Layer.en_US.vtt | 1.7 KB | ||
| 5 - Fully connected Layer.mp4 | 9.4 MB | ||
| 5 - Loading the dataset.en_US.vtt | 1.1 KB | ||
| 5 - Loading the dataset.mp4 | 9.2 MB | ||
| 5 - Long Short-Term Memory (LSTM) networks.en_US.vtt | 1.3 KB | ||
| 5 - Long Short-Term Memory (LSTM) networks.mp4 | 6.5 MB | ||
| 5 - Softmax function.en_US.vtt | 819.2 B | ||
| 5 - Softmax function.mp4 | 4.2 MB | ||
| 6 - Hopfield neural network.en_US.vtt | 1.1 KB | ||
| 6 - Hopfield neural network.mp4 | 5.3 MB | ||
| 6 - Rectified Linear Unit (ReLU) function.en_US.vtt | 1.4 KB | ||
| 6 - Rectified Linear Unit (ReLU) function.mp4 | 5.3 MB | ||
| 6 - Splitting the dataset into independent and dependent variables.en_US.vtt | 2.8 KB | ||
| 6 - Splitting the dataset into independent and dependent variables.mp4 | 22.8 MB | ||
| 7 - Boltzmann Machine Neural Network.en_US.vtt | 819.2 B | ||
| 7 - Boltzmann Machine Neural Network.mp4 | 4.7 MB | ||
| 7 - Label encoding using scikit-learn.en_US.vtt | 3.9 KB | ||
| 7 - Label encoding using scikit-learn.mp4 | 28 MB | ||
| 7 - Leaky Rectified Linear Unit function.en_US.vtt | 819.2 B | ||
| 7 - Leaky Rectified Linear Unit function.mp4 | 4 MB | ||
| 8 - One-hot encoding using scikit-learn.en_US.vtt | 5.8 KB | ||
| 8 - One-hot encoding using scikit-learn.mp4 | 37.9 MB | ||
| 9 - Training and Test Sets Splitting Data.en_US.vtt | 3.1 KB | ||
| 9 - Training and Test Sets Splitting Data.mp4 | 26.4 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 124 total files | |||
Python for Deep Learning: Build Neural Networks in Python
https://WebToolTip.com
Last updated 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 4m | Size: 785 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
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1 GB | freecoursewb | 20 hours | 0 | 0 | |
|
Udemy - Python Programming for Beginners - Learn Python from Scratch Posted by
freecoursewb in Other
|
3.9 GB | freecoursewb | 20 hours | 0 | 0 |
| 1.2 GB | freecoursewb | 20 hours | 16 | 16 | |
| 1.9 GB | freecoursewb | 3 weeks | 3 | 74 | |
| 1.2 GB | freecoursewb | 3 weeks | 30 | 4 |
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