Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models - amanchadha/coursera-deep ...
shanuhalli/Assignment-Neural-Networks
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.
GitHub
This specialisation has five courses. Courses: Course 1: Neural Networks and Deep Learning. Learning Objectives : Understand the major technology trends driving Deep Learning. Be able to build, train and apply fully connected deep neural networks. Know how to implement efficient (vectorized) neural networks.
Building your Deep Neural Network: Step by Step
Building a deeper neural network (with more than 1 hidden layer) Implementing an easy-to-use neural network class; This was indeed a long assignment, but the next part of the assignment is easier. ;) In the next assignment, you'll be putting all these together to build two models: A two-layer neural network; An L-layer neural network
Building your Deep Neural Network: Step by Step
In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will be able to: Use non-linear units like ReLU to improve your model. Build a deeper neural network (with more than 1 hidden layer) Implement an easy-to-use neural network class.
Deep Neural Network for Image Classification: Application
Deep Neural Network for Image Classification: Application. When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat ...
Neural Networks and Deep Learning
Week 2: Neural Networks Basics. Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models. Quiz 2: Neural Network Basics; Programming Assignment: Python Basics With Numpy; Programming Assignment: Logistic Regression with a Neural Network mindset; Week 3: Shallow neural networks
Deep Learning Specialization Coursera [UPDATED Version 2021]
What's New. This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks. One of the most major changes was shifting from Tensorflow 1 to Tensorflow 2. Also, new materials were added. However, Most of the old online repositories still don't have old codes. This repo contains updated ...
Improving Deep Neural Networks: Hyperparameter tuning, Regularization
View on GitHub Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results ...
Convolutional Neural Networks
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images ...
GitHub
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main. Cannot retrieve latest commit at this time. 2 Commits. Assignment 16 -Neural Network -ForestFires.ipynb. Assignment 16.
Building your Deep Neural Network: Step by Step
This week, you will build a deep neural network, with as many layers as you want! In this notebook, you will implement all the functions required to build a deep neural network. In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will be able to: Notation: layer.
Assignment 1
Assignment 1. Artificial Neural Networks. 1st Assignment - Shahid Beheshti University - Master's Program February 27, 2023. Due date: March 12. ** You are required to write a detailed report for both Theory and implementation tasks.**. Exercise 1. Why is it generally preferable to use a Logistic Regression classifier rather than a classical ...
Deep Neural Network Implementation Using PyTorch
PyTorch provides a variety of layer types, such as fully connected layers ( nn.Linear ), convolutional layers ( nn.Conv2d ), and recurrent layers ( nn.RNN ). These layers can be stacked together to form a deep neural network architecture. The list of available neural network layers, including but not limited to:
Building your Deep Neural Network: Step by Step
In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will be able to: Use non-linear units like ReLU to improve your model. Build a deeper neural network (with more than 1 hidden layer) Implement an easy-to-use neural network class.
Neural Network Assignment
Neural Networks Assignment Problem Set 1 (60 points. 10 points per question) The TensorFlow Playground is a handy neural network simulator built by the TensorFlow team. In this exercise, you will train several binary classifiers in just a few clicks, and tweak the model's architecture and its hyperparameters to gain some intuition on how ...
ahsan-83/Deep-Learning-Specialization-Coursera
Week 2-Programming Assignment Python Basics with numpy; Week 2-Programming Assignment Logistic Regression with a Neural Network mindset; Week 3-Programming Assignment Planar data classification; Week 4-Programming Assignment Building your deep neural network Step by Step; Week 4-Programming Assignment Deep Neural Network Application
Neural Networks Assignment (forestfires) · GitHub
Neural Networks Assignment (forestfires).ipynb This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Sequence Models
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. Assignment of Week 1. Quiz 1: Recurrent Neural Networks
fanghao6666/neural-networks-and-deep-learning
This is my assignment on Andrew Ng's special course "Deep Learning Specialization" This special course consists of five courses:Neural Networks and Deep Learning ; Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects.
A simple regression neural network using PyTorch. The RegressionANN class defines a model with a customizable hidden layer size, specified by the hln parameter. The network includes an input layer, a hidden layer with ReLU activation, and an output layer. - the-y9/Bonus-assignment-1---May-2024---Deep-Learning
GitHub
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main. Cannot retrieve latest commit at this time. 2 Commits. Neural_Network_Assignment (Forest_Fire).ipynb. Neural_Networks_Assignment (Turbine_data).ipynb.
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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models - amanchadha/coursera-deep ...
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.
This specialisation has five courses. Courses: Course 1: Neural Networks and Deep Learning. Learning Objectives : Understand the major technology trends driving Deep Learning. Be able to build, train and apply fully connected deep neural networks. Know how to implement efficient (vectorized) neural networks.
Building a deeper neural network (with more than 1 hidden layer) Implementing an easy-to-use neural network class; This was indeed a long assignment, but the next part of the assignment is easier. ;) In the next assignment, you'll be putting all these together to build two models: A two-layer neural network; An L-layer neural network
In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will be able to: Use non-linear units like ReLU to improve your model. Build a deeper neural network (with more than 1 hidden layer) Implement an easy-to-use neural network class.
Deep Neural Network for Image Classification: Application. When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat ...
Week 2: Neural Networks Basics. Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models. Quiz 2: Neural Network Basics; Programming Assignment: Python Basics With Numpy; Programming Assignment: Logistic Regression with a Neural Network mindset; Week 3: Shallow neural networks
What's New. This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks. One of the most major changes was shifting from Tensorflow 1 to Tensorflow 2. Also, new materials were added. However, Most of the old online repositories still don't have old codes. This repo contains updated ...
View on GitHub Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results ...
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images ...
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main. Cannot retrieve latest commit at this time. 2 Commits. Assignment 16 -Neural Network -ForestFires.ipynb. Assignment 16.
This week, you will build a deep neural network, with as many layers as you want! In this notebook, you will implement all the functions required to build a deep neural network. In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will be able to: Notation: layer.
Assignment 1. Artificial Neural Networks. 1st Assignment - Shahid Beheshti University - Master's Program February 27, 2023. Due date: March 12. ** You are required to write a detailed report for both Theory and implementation tasks.**. Exercise 1. Why is it generally preferable to use a Logistic Regression classifier rather than a classical ...
PyTorch provides a variety of layer types, such as fully connected layers ( nn.Linear ), convolutional layers ( nn.Conv2d ), and recurrent layers ( nn.RNN ). These layers can be stacked together to form a deep neural network architecture. The list of available neural network layers, including but not limited to:
In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will be able to: Use non-linear units like ReLU to improve your model. Build a deeper neural network (with more than 1 hidden layer) Implement an easy-to-use neural network class.
Neural Networks Assignment Problem Set 1 (60 points. 10 points per question) The TensorFlow Playground is a handy neural network simulator built by the TensorFlow team. In this exercise, you will train several binary classifiers in just a few clicks, and tweak the model's architecture and its hyperparameters to gain some intuition on how ...
Week 2-Programming Assignment Python Basics with numpy; Week 2-Programming Assignment Logistic Regression with a Neural Network mindset; Week 3-Programming Assignment Planar data classification; Week 4-Programming Assignment Building your deep neural network Step by Step; Week 4-Programming Assignment Deep Neural Network Application
Neural Networks Assignment (forestfires).ipynb This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. Assignment of Week 1. Quiz 1: Recurrent Neural Networks
This is my assignment on Andrew Ng's special course "Deep Learning Specialization" This special course consists of five courses:Neural Networks and Deep Learning ; Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
nileshlondhe4536. /. Neural Networks Assignment (forestfires).ipynb. Neural Networks Assignment (forestfires) Neural Networks Assignment (forestfires). GitHub Gist: instantly share code, notes, and snippets.
This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects.
A simple regression neural network using PyTorch. The RegressionANN class defines a model with a customizable hidden layer size, specified by the hln parameter. The network includes an input layer, a hidden layer with ReLU activation, and an output layer. - the-y9/Bonus-assignment-1---May-2024---Deep-Learning
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main. Cannot retrieve latest commit at this time. 2 Commits. Neural_Network_Assignment (Forest_Fire).ipynb. Neural_Networks_Assignment (Turbine_data).ipynb.