amanchadha / coursera-deep-learning-specialization

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

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🧠 I currently head a Generative AI R&D team for Amazon AWS. Previously, I led AI for Speaker Understanding and Personalization at Amazon Alexa.

⌛️ Prior to Amazon, I was part of the Machine Intelligence Neural Design (MIND) team at Apple, where I specialized in designing AI models for a wide range of applications including Natural Language Processing, Speech Recognition, and Computer Vision. Prior to that, I was one of the early architects of Apple's M1 chip, which involved developing models for estimating performance of future Macs years in advance. Before Apple, I spent a couple of years at Nvidia and Qualcomm working on GPUs and ML Accelerators. I am a recipient of the EB-1 "Einstein Visa" (conferred by the US Government for extraordinary ability and acclaim in a specialized field).

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coursera-deep-learning-specialization's Issues

This Solution isn't upto date

This Solution isn't upto date as i looked up into the matter, the updated version requires more packages to be initiated

import numpy as np
import copy
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
from public_tests import *

%matplotlib inline
%load_ext autoreload
%autoreload 2

and so with this we will have different solution

TensorFlow train/test .h5 files

For some unknown reason, the .HDF5 train/test data is inaccessible neither when downloaded, nor when the project is cloned on VS code.
Could you please provide a clarification and solution concerning this issue.

C5_W4_A1_Transformer_Subclass_v1

Hi, I think you have a problem with your solution in this Assignment.
Maybe Coursera make an update to this Assignment.

Some of the weight files (csv) are zipped

Hi Aman,

Thanks for uploading this repo. In Course 4, week 4 assignment, some of the weight files of FaceNet has not been unzipped properly. While cloning and running the code its throwing error. Please check and update.

Thanks

C4-Week2 KerasTutorial datasets train_happy.h5.zip is unzip error

setup.sh can't download data

Tried to use git-lfs pull, doesn't work either.

C4-W4: no keras-facenet-h5/model.h5 file

model.load_weights('keras-facenet-h5/model.h5')
no weights file in this folder, it is appreciated if you can upload one.

Kernel Restarting : C2, Week 3

Hello everyone,

So i tried to run the code in the file "Tensorflow_Introduction_new.ipybd" part of C2, Week3
but when it comes at the part of plotting images, the Kernel restarts.

Additionally when i am trying to plot the same data combining Numpy & Matplotlib, hence using another Format,
the images are plotted with no issues in this case.

----------------------------------

images_iter = iter(x_train)
labels_iter = iter(y_train)
plt.figure(figsize=(10, 10))
for i in range(25):
ax = plt.subplot(5, 5, i + 1)
plt.imshow(next(images_iter).numpy().astype("uint8"))
plt.title(next(labels_iter).numpy().astype("uint8"))
plt.axis("off")

----------------------------------

Extra Info: Apparently there is no problem to plot the data before running the following code,
after this part i am no longer able to plot any data because the Kernel Restarts

----------------------------------

x_train = tf.data.Dataset.from_tensor_slices(train_dataset['train_set_x'])
y_train = tf.data.Dataset.from_tensor_slices(train_dataset['train_set_y'])

x_test = tf.data.Dataset.from_tensor_slices(test_dataset['test_set_x'])
y_test = tf.data.Dataset.from_tensor_slices(test_dataset['test_set_y'])

----------------------------------

Has anyone else countered this problem before !?

Thank you in advance !

I am using "TensorFlow v2.10" and i have already update Matplotlib at its newest version.

Image Segmentation with U-Net dataset

Hi,

Could you put the dataset folders into repo if it's not a problem? (cameraRGB, cameraMask)

I understand that I could find other dataset (but I don't know if does it matter somehow?)

Best regards

Cat vs non cat dataset

There is a problem in loading cat vs non cat dataset.
Its giving "OSError: Unable to open file (file signature not found)" while loading dataset.
I think , the dataset is not upoaded properly.May u plz look into it.

while building neutral network compute function has given 3 parameters instead of 2

solution: just remove "parameters" parameter and it will be fine

reference :In Planar_data_classification_with_one_hidden_layer in neural network and deep learning

LaTeX compilation/syntax error in C1/Week2/Python Basics...

The LaTeX code is visible in raw format under the section "Broadcasting the Softmax Function"as it has a syntax error

C2 - Week 1 - PA 2 - Regularization - data.mat

I cannot manage to download the dataset data.mat for C2 - PA2. This is the link to the file in this GitHub:
https://github.com/amanchadha/coursera-deep-learning-specialization/blob/6ebc4d60a90c1592aee3eaa3113eb4b37d9b4b19/C2%20-%20Improving%20Deep%20Neural%20Networks%20Hyperparameter%20tuning,%20Regularization%20and%20Optimization/Week%201/Regularization/datasets/data.mat
Can anybody share a download link to a file, e.g., via OneDrive, Google Drive, or any other similar service?

Much appreciated!

Datasets

Can u plz also provide with the datasets of programming assignments

not opening datasets

Actually , i have lost my subscription from coursera so i m not able to access this course.All the .h5 files u have uploaded is not working and giving the error "OSError: Unable to open file (file signature not found)".U can check this here "https://stackoverflow.com/questions/38089950/error-opening-file-in-h5py-file-signature-not-found".
If u may uplaod the correct files ,it will be really helpful.
Thanks in advance

Lower accuracy problem

Error debbuging C4-Week2-Assignment1

Hello
In course 4 (ConvNets), Week2, Assignment 1 (ResNets) , section 3.2 (The convolutional block), I did the implementation myself and got an error. When I looked at this solution, I find it exactly the same as mine but gives the same error:

I am getting the following error in the function "convolutional block":

---

AssertionError Traceback (most recent call last)
Input In [13], in <cell line: 14>()
12 assert type(A) == EagerTensor, "Use only tensorflow and keras functions"
13 assert tuple(tf.shape(A).numpy()) == (3, 2, 2, 6), "Wrong shape."
---> 14 assert np.allclose(A.numpy(), convolutional_block_output1), "Wrong values when training=False."
15 print(A[0])
17 B = convolutional_block(X, f = 2, filters = [2, 4, 6], training=True)

AssertionError: Wrong values when training=False.

---

Can you please advise?

Thanks,

Boris M.

Dataset of MobileNet

Where can I find the dataset of C4W2 MobileNet assignment?
Did I misunderstand the meaning of the description? Where should the dataset mentioned in the description be located?

Missing Notes on Course 4

There are missing "MobileNet", "MobileNet Architecture" and "EfficientNet" notes.

Broken links to 2021 version

Links to Programming Assignments from 2021 version are broken:

Can i get unsolved assignment

ValueError: Unknown mat file type, version 54, 48

In Week 1-> regularization notebook -> I canot load dataset

1 x_train, y_train, X_test, y_test = load_2D_dataset()

3 frames
[/usr/local/lib/python3.10/dist-packages/scipy/io/matlab/_miobase.py](https://localhost:8080/#) in _get_matfile_version(fileobj)
    247     if maj_val in (1, 2):
    248         return ret
--> 249     raise ValueError('Unknown mat file type, version %s, %s' % ret)
    250 
    251 

ValueError: Unknown mat file type, version 54, 48

I run the setup.sh in MacOS terminal, but no data was downloaded. Please help.

YOLO example model?

Hi,

Got an error when loading the model in YOLO Car detection example when I run the notebook on my computer (with all files downloaded). Is it an issue only with Tensorflow version or something else is going on? Is there any hotfix for this issue so I can make it work? I've finished the course few months ago and on Coursera everything worked fine.

WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.

and error:
OpError: NewRandomAccessFile failed to Create/Open: model_data/variables\variables.data-00000-of-00001 : Nie mozna odnalezc okreslonego pliku. ; No such file or directory

PS. After completing the course I don't have access to original assignment so I can't open it back on Coursera.

data.mat file is not loading in Course 2, Week 3

Unknown mat file type, version 54, 48 error thrown while loading the 2D dataset (data. mat). Also, this file is not opening with MatLab.

Course1 - Week 2 - PA 2 - Logistic Regression with a Neural Network mindset

In part 2 where we try to reshape the training and test examples, i.e. to flatten matrix "train_set_x_orig" and "test_set_x_orig".

In this note, the author uses "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3],train_set_x_orig.shape[0])", which fails in making every colum as a training example and a wrong result of train accuracy and test accuracy.

I think "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T" would be better.

please feel free to discuss this:)

Can't git lfs pull

Hi I've been trying to download the different datasets.

I've tried different solutions such as git lfs pull or https://stackoverflow.com/questions/62905325/this-repository-is-over-its-data-quota-account-responsible-for-lfs-bandwidth-sh/68113604#68113604https://stackoverflow.com/questions/62905325/this-repository-is-over-its-data-quota-account-responsible-for-lfs-bandwidth-sh/68113604#68113604
But I always get the same issue refering to

batch response: This repository is over its data quota. Account responsible for LFS bandwidth should purchase more data packs to restore access.
Failed to fetch some objects from 'https://github.com/amanchadha/coursera-deep-learning-specialization.git/info/lfs

Any chance we can download the datasets from somewhere else or participate in the fees for the account to purchase more data packs?

Thanks!

C5_W4_A1_Transformer_EX3_scaled_attention_logits

Your scaled_attention_logits is calculated wrong, since it gives:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-41-00665b20febb> in <module>
      1 # UNIT TEST
----> 2 scaled_dot_product_attention_test(scaled_dot_product_attention)

~/work/W4A1/public_tests.py in scaled_dot_product_attention_test(target)
     73     assert np.allclose(weights, [[0.30719590187072754, 0.5064803957939148, 0.0, 0.18632373213768005],
     74                                  [0.3836517333984375, 0.3836517333984375, 0.0, 0.2326965481042862],
---> 75                                  [0.3836517333984375, 0.3836517333984375, 0.0, 0.2326965481042862]]), "Wrong masked weights"
     76     assert np.allclose(attention, [[0.6928040981292725, 0.18632373213768005],
     77                                    [0.6163482666015625, 0.2326965481042862],

AssertionError: Wrong masked weights

The correct value should be:

if mask is not None: # Don't replace this None
        scaled_attention_logits += ( (1-mask) * -1e9 )

Cheers,

setup.sh not working

Hi,

I cloned the repository on my system. I set permissions to execute setup.sh file. On running setup.sh, I am getting following error -

Archive:  ./C5 - Sequence Models/Week 2/Word Vector Representation/data/glove.6B.50d.txt.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
unzip:  cannot find zipfile directory in one of ./C5 - Sequence Models/Week 2/Word Vector Representation/data/glove.6B.50d.txt.zip or
        ./C5 - Sequence Models/Week 2/Word Vector Representation/data/glove.6B.50d.txt.zip.zip, and cannot find ./C5 - Sequence Models/Week 2/Word Vector Representation/data/glove.6B.50d.txt.zip.ZIP, period.

Archive:  ./C5 - Sequence Models/Week 2/Emojify/data/glove.6B.50d.txt.zip
  End-of-central-directory signature not found.  Either this file is not
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unzip:  cannot find zipfile directory in one of ./C5 - Sequence Models/Week 2/Emojify/data/glove.6B.50d.txt.zip or
        ./C5 - Sequence Models/Week 2/Emojify/data/glove.6B.50d.txt.zip.zip, and cannot find ./C5 - Sequence Models/Week 2/Emojify/data/glove.6B.50d.txt.zip.ZIP, period.

Archive:  ./C5 - Sequence Models/Week 3/Trigger word detection/XY_dev/X_dev.npy.zip
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unzip:  cannot find zipfile directory in one of ./C5 - Sequence Models/Week 3/Trigger word detection/XY_dev/X_dev.npy.zip or
        ./C5 - Sequence Models/Week 3/Trigger word detection/XY_dev/X_dev.npy.zip.zip, and cannot find ./C5 - Sequence Models/Week 3/Trigger word detection/XY_dev/X_dev.npy.zip.ZIP, period.

Archive:  ./C5 - Sequence Models/Week 3/Trigger word detection/models/tr_model.h5.zip
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unzip:  cannot find zipfile directory in one of ./C5 - Sequence Models/Week 3/Trigger word detection/models/tr_model.h5.zip or
        ./C5 - Sequence Models/Week 3/Trigger word detection/models/tr_model.h5.zip.zip, and cannot find ./C5 - Sequence Models/Week 3/Trigger word detection/models/tr_model.h5.zip.ZIP, period.

Archive:  ./C5 - Sequence Models/Week 3/Trigger word detection/XY_train/X.npy.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
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unzip:  cannot find zipfile directory in one of ./C5 - Sequence Models/Week 3/Trigger word detection/XY_train/X.npy.zip or
        ./C5 - Sequence Models/Week 3/Trigger word detection/XY_train/X.npy.zip.zip, and cannot find ./C5 - Sequence Models/Week 3/Trigger word detection/XY_train/X.npy.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 2/ResNets/datasets/train_signs.h5.zip
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unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 2/ResNets/datasets/train_signs.h5.zip or
        ./C4 - Convolutional Neural Networks/Week 2/ResNets/datasets/train_signs.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 2/ResNets/datasets/train_signs.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 2/ResNets/ResNet50.h5.zip
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unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 2/ResNets/ResNet50.h5.zip or
        ./C4 - Convolutional Neural Networks/Week 2/ResNets/ResNet50.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 2/ResNets/ResNet50.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 2/KerasTutorial/datasets/train_happy.h5.zip
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        ./C4 - Convolutional Neural Networks/Week 2/KerasTutorial/datasets/train_happy.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 2/KerasTutorial/datasets/train_happy.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 3/Car detection for Autonomous Driving/model_data/yolo.h5.zip
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unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 3/Car detection for Autonomous Driving/model_data/yolo.h5.zip or
        ./C4 - Convolutional Neural Networks/Week 3/Car detection for Autonomous Driving/model_data/yolo.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 3/Car detection for Autonomous Driving/model_data/yolo.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/nn4.small2.v7.h5.zip
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        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/nn4.small2.v7.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/nn4.small2.v7.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_3x3_conv2_w.csv.zip
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        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_3x3_conv2_w.csv.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_3x3_conv2_w.csv.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4a_1x1_conv_w.csv.zip
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Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4e_3x3_conv2_w.csv.zip
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Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5b_3x3_conv2_w.csv.zip
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Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4a_3x3_conv2_w.csv.zip
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Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_1x1_conv_w.csv.zip
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unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_1x1_conv_w.csv.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_1x1_conv_w.csv.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5a_1x1_conv_w.csv.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_3c_3x3_conv2_w.csv.zip
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unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_3c_3x3_conv2_w.csv.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_3c_3x3_conv2_w.csv.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_3c_3x3_conv2_w.csv.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4e_5x5_conv2_w.csv.zip
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unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4e_5x5_conv2_w.csv.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4e_5x5_conv2_w.csv.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_4e_5x5_conv2_w.csv.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5b_1x1_conv_w.csv.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5b_1x1_conv_w.csv.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5b_1x1_conv_w.csv.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/weights/inception_5b_1x1_conv_w.csv.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_face.h5.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_face.h5.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_face.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_face.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/test_happy.h5.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/test_happy.h5.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/test_happy.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/test_happy.h5.zip.ZIP, period.

Archive:  ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_happy.h5.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
unzip:  cannot find zipfile directory in one of ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_happy.h5.zip or
        ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_happy.h5.zip.zip, and cannot find ./C4 - Convolutional Neural Networks/Week 4/Face Recognition/datasets/train_happy.h5.zip.ZIP, period.

Archive:  ./C2 - Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 3/datasets/train_signs.h5.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
unzip:  cannot find zipfile directory in one of ./C2 - Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 3/datasets/train_signs.h5.zip or
        ./C2 - Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 3/datasets/train_signs.h5.zip.zip, and cannot find ./C2 - Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 3/datasets/train_signs.h5.zip.ZIP, period.

./setup.sh: line 8: ./C4 - Convolutional Neural Networks/Week 4/Neural Style Transfer/pretrained-model/imagenet-vgg-verydeep-19.mat: No such file or directory

On running file command with one of the zip files I get following -

file glove.6B.50d.txt.zip
glove.6B.50d.txt.zip: ASCII text
With 7z command I get following -

7z e glove.6B.50d.txt.zip

7-Zip [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21
p7zip Version 16.02 
Scanning the drive for archives:
1 file, 133 bytes (1 KiB)

Extracting archive: glove.6B.50d.txt.zip
ERROR: glove.6B.50d.txt.zip
glove.6B.50d.txt.zip
Open ERROR: Can not open the file as [zip] archive

ERRORS:
Is not archive

Can't open as archive: 1
Files: 0
Size:       0
Compressed: 0

Can you provide the correct zip files?

Reduce_mean to be changed to reduce_sum for better results | Course 2 W3A1

To quote the notebook itself

"
In step 1, the compute_total_loss function will only take care of summing the losses from one mini-batch of samples. Then, as you train the model (in section 3.3) which will call this compute_total_loss function once per mini-batch, step 2 will be done by accumulating the sums from each of the mini-batches, and finishing it with the division by the total number of samples to get the final cost value.

Computing the "total loss" instead of "mean loss" in step 1 can make sure the final cost value to be consistent. For example, if the mini-batch size is 4 but there are just 5 samples in the whole batch, then the last mini-batch is going to have 1 sample only. Considering the 5 samples, losses to be [0, 1, 2, 3, 4] respectively, we know the final cost should be their average which is 2. Adopting the "total loss" approach will get us the same answer. However, the "mean loss" approach will first get us 1.5 and 4 for the two mini-batches, and then finally 2.75 after taking average of them, which is different from the desired result of 2. Therefore, the "total loss" approach is adopted here.

"

Problem downloading h5 files on course 2 week 3

Hi!

First of all, thanks for all the content, It's helping me a lot.

I'm a newbie on GitHub, so maybe It's my mistake. I've clone the entire repository and then I tried to use the following command to get de h5 file:

• git lfs pull

But I'm getting the next error message: This repository is over its data quota. Account responsible for LFS bandwidth should purchase more data packs to restore access.
Failed to fetch some objects from 'https://github.com/amanchadha/coursera-deep-learning-specialization.git/info/lfs'

I would appreciate any help.

Thanks on advice !!

PD. I found here how to proceed: https://stackoverflow.com/questions/58078170/github-how-to-download-hdf5-file

Slight update in Function definition

For Module 2 i.e Hyper Parameter Tuning , Week 1 lab's functions have been modified to print the value as well. Hence there is a slight update in function definition where you need to add a parameter : print_msg = False.

C2-W2 Optimization_methods task update_parameters_with_adam needs updatation

The older function used to return 3 variables parameters,v,s like

return parameters,v,s

At the end of the function but for now the tests got updated and now it wants to get 5 variables returned to test the function. So the function here also needs to be updated to reflect that.

See the newer version of test it wants to unpack 5 values from the function in place of 3 as with the previous versions

This is the older code in this repo that returned 3 variables only. I have included the older version of test also here that wanted to unpack 3 variables.

Not know how to run setup.sh

Anyone can guide me how to run setup.sh?

Cannot test our own images

C1 - Week 3 - PA 3 - Planar data classification with one hidden layer

The function "plot_decision_boundary(model, X, y)" does not work to plot the decision boundaries according to the trained neural network due to incorrect shapes. If you want to plot the decision boundaries for "4.5 Predictions" and "4.6 - Tuning hidden layer size (optional/ungraded exercise)", use the following function instead (place within the same .py file as your function "predict(parameters, X)", otherwise you'll need to import the functions):

def plot_decision_boundary_nn(parameters, X, Y):
    # Set min and max values and give it some padding
    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    grid_points = np.c_[xx.ravel(), yy.ravel()].T
    # Predict the function value for the whole grid
    Z = predict(parameters, grid_points)
    Z = Z.reshape(xx.shape)
    # Plot the contour and training examples
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    plt.ylabel('x2')
    plt.xlabel('x1')
    plt.scatter(X[0, :], X[1, :], c=Y, cmap=plt.cm.Spectral)

Sequence Models - Week3 - Trigger Word Detection

Hi and thanks for your repository...
It seems that there is a problem in "Trigger_word_detection_v2a.ipynb" file...
After "create_training_example" method, its test asserts norm of Spectrogram and it should be close to 39745552.52075 and there is a problem in this assertion because it failed and throw an exception. a notable issue is that when we execute this test, the "create_training_example" function add two "activates" and also two "negatives" (based on a copy of your presented function) but in your file based on the test output there is three added audios on background. maybe this is the reason of this problem in you file. please check and debug if you find the problem.

best regards...

h5 files corrupted/not-accessible in neural network and deep learning assignment

Can you please change the h5 files?