Beginner’s Guide to TensorFlow 2.0: Deep Learning Demo with Simplilearn

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TensorFlow 2.0 is an open-source deep learning framework developed by Google that provides a set of tools and libraries for building and deploying machine learning models. It is widely used for a variety of applications, including image recognition, natural language processing, and generative models. In this tutorial, we will walk you through the basics of TensorFlow 2.0 and show you how to build and train a simple neural network using this powerful framework.

Before we get started, make sure you have TensorFlow 2.0 installed on your machine. You can do this by running the following command in your terminal:

pip install tensorflow

Now that you have TensorFlow installed, let’s dive into building a simple neural network. In this example, we will build a model to classify handwritten digits from the MNIST dataset.

First, let’s import the necessary libraries:

import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt

Next, let’s load the MNIST dataset:

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

Now, let’s preprocess the data by normalizing the pixel values and reshaping the input data:

x_train, x_test = x_train / 255.0, x_test / 255.0
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

Next, let’s define our neural network model. We will use a simple convolutional neural network for this example:

model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])

Now, let’s compile the model with an appropriate loss function and optimizer:

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

Next, let’s train the model on the training data:

history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test))

After training the model, we can evaluate its performance on the test data:

test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)

Finally, let’s visualize the training and validation accuracy over epochs:

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='upper left')
plt.show()

Congratulations! You have successfully built and trained a simple neural network using TensorFlow 2.0. This is just a basic example of what you can achieve with TensorFlow. There are many more advanced features and techniques that you can explore to build more complex machine learning models.

I hope this tutorial helps you get started with TensorFlow 2.0 and inspires you to delve deeper into the world of deep learning. Happy coding!

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@anandperi7060
1 month ago

I'm wondering if the objective of the tutorial/model is to just train and predict based on one FEATURE (Temp); why all those bunny trails about humidity, date conversions, quantile distributions, for loops etc. It is clear the instructor is reading the code and pasting it and not knowledgeable about it from the perspective he was good.

Always keep the concepts simple especially if you are passing a single series of data called Temp and trying to predict from it… way over complicated for the end result.

@reeddowns1129
1 month ago

Great Video! I'd rather not share my email on YouTube. How else can I view the notebook?

One suggestion– the csv file in the google drive provided is all in one column. A quick fix is to open notepad and replace all spaces with commas.

@ayenewyihune
1 month ago

I bet Tensorflow developers have so much love for Matlab

@ayenewyihune
1 month ago

Thank you tf 2 for avoiding those horror parts which made me fear Tensorflow

@natashasamuel9346
1 month ago

Great class.
Keep up the good work.

Thank You,
Natasha Samuel

@MrXXAntonXx
1 month ago

Honestly, I wish this tutorial was a bit more focused. You are patient in your explanations, but I really would have liked more on the model set up in TF with maybe a quick sketch on how the network you build looks like. Instead you talked a lot about pandas formatting and visualization which doesn't fit the title of the video.

@TheWhyNotSeries
1 month ago

Love it!

@TorontoWangii
1 month ago

Great video! Thanks for the simple and straightforward explanation.

@medinikb
1 month ago

Nicely explained. Thank you for sharing your knowledge.

@dongyufeng9380
1 month ago

Hi, can I get the Jupiter notebook plz?

@johnmulhall5625
1 month ago

Did you just say "num-pee" lol. j/k great video

@jgubash100
1 month ago

Very good explanations

@amirtaghavy7647
1 month ago

Great start and clear explanation of the big picture and process flow… too much beating around the bush on non-relevant topics (e.g., data curation) in the mid sections of video.

@SimplilearnOfficial
1 month ago

Thanks for watching the video. The link for the dataset used in the video is provided in the description. Thanks!

@faezefathi5387
1 month ago

Thank you. It was very helpful.

@thiencong9810
1 month ago

Thanks for your videos, can you send me the "air_quality.csv" to my email. congthien.en@gmail.com, I already subscribe your channel and hit the like to your video

@yuantang2807
1 month ago

Amazing tutorial! Can you sent me the air_quality dataset plz! holatangyuan@gmail.com

@sriruksrithongchai6852
1 month ago

Great Lectures. Could you please send me the data to the email: ssrithongchai@gmail.com? Thank you very much.

@hitaishiroy2871
1 month ago

@Simplilearn Could you please send me the air quality csv file used in the video? Mail id: hitaishi.roysep@gmail.com