Tensorflow ppt used for my gdsc event.

1. VISHNU INSTITUTE OF TECHNOLOGY

2. Kousik
SPEAKER

3. CONTENTS
TensorFlow Fundamentals
Regression using TensorFlow
Classification using
Tensorflow
Convolutional Neural
Networks

4. Why Tensorflow.........?
• TensorFlow is an open-source machine learning framework developed by
Google.
• It provides a comprehensive ecosystem of tools, libraries, and community
resources for building and deploying machine learning models.

5. Concepts:
⚬ Tensors: The fundamental building blocks in TensorFlow, representing multi-dimensional
arrays of data.
⚬ Graphs: TensorFlow uses a computational graph to represent the flow of data through a
series of operations.
⚬ Operations: Nodes in the graph that perform computations on tensors.
Sessions: Execution environments for running operations and evaluating tensors.
Features:
⚬ Scalability: TensorFlow is designed to scale from a single device to large distributed
systems.
⚬ Flexibility: It supports a wide range of machine learning tasks, including classification,
regression, clustering, and more.
⚬ High-Level APIs: TensorFlow offers high-level APIs like Keras for building and training
models with ease.
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6. Regression in Tensorflow
• Introduction: Predicting continuous outcomes from data.
• Key components: input features, target variable, model, loss
function.
• TensorFlow's utility: seamless implementation of regression
models.
• Live demonstration: explore regression with a practical example.
• Witness the power of TensorFlow in predictive analytics!

7. Classification in Tensorflow
1.Introduction: Categorizing data into distinct classes or
categories.
2.Key components: input features, class labels, model
architecture, loss function.
3.TensorFlow's advantage: streamlined implementation of
classification algorithms.
4.Live demonstration: delve into classification with a hands-on
example.
5.Experience the effectiveness of TensorFlow in classifying data!

8. Convolutional layer in TensorFlow
Introduction: Specialized neural networks for image recognition and
processing.
1.Key components: convolutional layers, pooling layers, fully
connected layers.
2.TensorFlow's support: seamless integration of CNN architectures.
3.Live demonstration: explore CNNs with a practical image
recognition task.
4.Witness the transformative power of CNNs in TensorFlow!

9. THANK
YOU