mayank-singh-sikarwar/DeepLearning_Code_Demo

📝the assignment of Andrew Ng's deep learning courses in Coursera

CONTENTS

This repository is my own coding assignment of Andrew Ng's deep learning courses in Coursera.In addition, I also put the original .ipynb files and data in the corresponding folders.

If you find something wrong, please feel free to contact me : jeff.xinsc@gmail.com.

If this repository is helpful to you, welcome to star or fork.

‼️More content about deep learning👉shichaoxin.com.

【Demo1】

📌assignment2_1: Python Basics with Numpy

1. Be able to use numpy functions and numpy matrix/vector operations
2. Understand the concept of "broadcasting"
3. Be able to vectorize code

📌assignment2_2: Logistic Regression with a Neural Network mindset

1. Build the general architecture of a learning algorithm, including:
   • Initializing parameters
   • Calculating the cost function and its gradient
   • Using an optimization algorithm (gradient descent)
2. Gather all three functions above into a main model function, in the right order

【Demo2】

📌assignment3：Planar data classification with one hidden layer

1. Implement a 2-class classification neural network with a single hidden layer
2. Use units with a non-linear activation function, such as tanh
3. Compute the cross entropy loss
4. Implement forward and backward propagation

【Demo3】

📌assignment4_1：Building your Deep Neural Network: Step by Step

1. Use non-linear units like ReLU to improve your model
2. Build a deeper neural network (with more than 1 hidden layer)
3. Implement an easy-to-use neural network class

📌assignment4_2：Deep Neural Network for Image Classification: Application

1. Build and apply a deep neural network to supervised learning

【Demo4】

📌assignment5_1：Initialization

1. Training your neural network requires specifying an initial value of the weights
2. Choose the initialization for a new neural network
3. A well chosen initialization can:
   • Speed up the convergence of gradient descent
   • Increase the odds of gradient descent converging to a lower training (and generalization) error

📌assignment5_2：Regularization

1. Use regularization in your deep learning models

📌assignment5_3：Gradient Checking

1. Implement and use gradient checking

【Demo5】

📌assignment6：Optimization Methods

1. Understand the intuition between Adam and RMS prop
2. Recognize the importance of mini-batch gradient descent
3. Learn the effects of momentum on the overall performance of your model

【Demo6】

📌assignment7：TensorFlow Tutorial

In this assignment, you will learn to do the following in TensorFlow:

1. Initialize variables
2. Start your own session
3. Train algorithms
4. Implement a Neural Network

【Demo7】

📌assignment1：Convolutional Neural Networks: Step by Step

In this assignment, you will :

1. implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.

📌assignment2：Convolutional Neural Networks: Application

In this assignment, you will :

1. Implement helper functions that you will use when implementing a TensorFlow model.
2. Implement a fully functioning ConvNet using TensorFlow.

After this assignment you will be able to:

1. Build and train a ConvNet in TensorFlow for a classification problem.

【Demo8】

📌assignment1：Keras tutorial - the Happy House

In this assignment, you will :

1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.
2. See how you can in a couple of hours build a deep learning algorithm.

📌assignment2：Residual Networks

In this assignment, you will :

1. Implement the basic building blocks of ResNets.
2. Put together these building blocks to implement and train a state-of-the-art neural network for image classification.

"ResNet50.h5" is too large (>100MB) to push to github.If you need this file, please email me.

【Demo9】

📌assignment1：Autonomous driving - Car detection

In this assignment, you will :

1. Learn about object detection using the very powerful YOLO model.
2. Use object detection on a car detection dataset.
3. Deal with bounding boxes.

"yolo.h5" is too large (>100MB) to push to github.If you need this file, please email me.

【Demo10】

📌assignment1：Face Recognition for the Happy House

In this assignment, you will :

1. Implement the triplet loss function.
2. Use a pretrained model to map face images into 128-dimensional encodings.
3. Use these encodings to perform face verification and face recognition.

📌assignment2：Deep Learning & Art: Neural Style Transfer

In this assignment, you will :

• Implement the neural style transfer algorithm.
• Generate novel artistic images using your algorithm.

"imagenet-vgg-verydeep-19.mat" is too large (>100MB) to push to github.If you need this file, please email me.

【Demo11】

📌assignment1：Building your Recurrent Neural Network - Step by Step

In this assignment, you will :

• Implement your first Recurrent Neural Network in numpy.

📌assignment2：Character level language model - Dinosaurus land

In this assignment, you will learn:

• How to store text data for processing using an RNN.
• How to synthesize data, by sampling predictions at each time step and passing it to the next RNN-cell unit.
• How to build a character-level text generation recurrent neural network.
• Why clipping the gradients is important.

📌assignment3：Improvise a Jazz Solo with an LSTM Network

In this assignment, you will :

• Apply an LSTM to music generation.
• Generate your own jazz music with deep learning.

【Demo12】

📌assignment1：Operations on word vectors

After this assignment you will be able to:

• Load pre-trained word vectors, and measure similarity using cosine similarity.
• Use word embeddings to solve word analogy problems such as Man is to Woman as King is to __.
• Modify word embeddings to reduce their gender bias.

📌assignment2：Emojify!

In this assignment, you will :

• Use word vector representations to build an Emojifier.

"glove.6B.50d.txt" is too large (>100MB) to push to github.If you need this file, please email me.

【Demo13】

📌assignment1：Neural Machine Translation

In this assignment, you will :

• Build a Neural Machine Translation (NMT) model to translate human readable dates ("25th of June, 2009") into machine readable dates ("2009-06-25").
• Use an attention model.

📌assignment2：Trigger Word Detection

In this assignment you will learn to：

• Structure a speech recognition project.
• Synthesize and process audio recordings to create train/dev datasets.
• Train a trigger word detection model and make predictions.

"Trigger word detection/XY_train/X.npy" is too large (>100MB) to push to github.If you need this file, please email me.

"Trigger word detection/XY_dev/X_dev.npy" is too large (>100MB) to push to github.If you need this file, please email me.