Neural Network For Handwritten Digits Classification | Deep Learning Tutorial 7 (Tensorflow2.0)

In this video we will build our first neural network in tensorflow and python for handwritten digits classification. We will first build a very simple neural network with only input and output layer. After that we will add a hidden layer and check how the performance of our model changes.

🔖 Hashtags 🔖
#handwrittendigitrecognition #tensorflowtutorial #handwritingrecognition #mnisttensorflowtutorial

Prerequisites for this series:   

#️⃣ Social Media #️⃣

[skojjalar]

Using 1 hidden layer (1000) & relu (remaining parameters untouched), I reached 0.9908 accuracy. Thanks a lot for this course.

[phaniauce]

Excellent Video.. I always follow the philosophy: First have something working with as much little theory as possible...and then play with parameters that increase curiosity and then dissect the theory..Makes life easy.. than the other way around..

[niveditadas]

Sir, you are a born teacher. The way you represent complex topics in simpler way is truly amazing. I really admire your hard work for designing such wonderful courses. Thank you sir.

[iva]

This is the most elegant solution of Hand-Written digits MNIST problem in the whole Internet! Thank you. I've learned so much! You have my sub and much respect!

[AlonAvramson]

from the Optimizers ['SGD', 'RMSprop', 'Adam', 'Adadelta', 'Adagrad', 'Adamax', 'Nadam', 'Ftrl'] with 5 Ephocs, Adamax got the highest score on Test samples. Ftrl got the lowest score.

[drmanishmadhavatripathi]

I become fan of you as your style of explaining the things. You explained the complex topics in such a lucid manner that audience could not be distracted for a single while.

[amineamaach]

Just love the way you're making things pretty simple. A big shout-out to you.

[aditiparetkar]

Thank you so much for the great explanation!
By using relu in the hidden layer and softmax in the output layer, was able to achieve an accuracy of 99%

[amaningoma]

Your course is more than explicit. Thank you so much for giving such efforts to share knowledge

[shoaibsaifi]

This is the only lecture where I got a really great idea about neural network. I have seen many lectures no one explain each thing like you did. They just make the neural network

[sand]

Even though you have mentioned 3Blue1brown over here you have explained Neural Network with much concrete example here. Your examples are very easy to visualise and understand for beginners and in some cases it's even better than the great AndrewNG himself. Being from the field of education I see all the qualities of a great teacher in you, keep up the good work and thank you for such a good tutorial for free.

[rkag]

Well lectured..Have to listen twice to understand this session accordingto my perception..

[pbptrwl]

What an amazing tutorial! Did a machine learning for the first time with so much clarity! Thank you so much sir

[nikhilkashyap]

Best Deep Learning Course I found On YouTube, I mean you the gave the content that I was looking for.

[arnavg]

THANK YOU, LOVE FROM INDIA .... GAYATRI, GAURAV (LOOSERS)

[mithleshsingh]

love the have you figure out the issue of accuracy, That makes a huge difference.

[cooleo]

This is phenomenal, thank you so much! Truly a brilliant teacher. Been looking for a while, and this video summed up so much. You sir earned yourself a subscriber, and perhaps many more as I share your channel. Thank you for taking the time to do this.

[parthshah]

You're god for the Beginner !!! The way you explain is way different than anyone else... SUPER AMAZING... HIGHLY RECOMMENDED

[sayedikramulhaqbanuree]

Amazing, I am completely new in the field but you explain in such a good manner. 10+ out of 10 stars.
I have done exercise with a small dataset all the samples are about 1797:

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

and then:
model = keras.Sequential([
keras.layers.Dense(100, activation='relu'),
keras.layers.Dense(10, activation='sigmoid')
])

model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

model.fit(X_train, y_train, epochs=20)

i have just increased number of epochs which given me a very good result of accuracy

once again thank you
from #Afghanistan

[PRABALBAISHYA-xifd]

Awesome tutorial ! I just increased the iterations to 10 and the accuracy turned out to be 99.55% !