More Related Content
Similar to Lecture Note DL&NN Tensorflow.pptx (20)
Recently uploaded (20)
![[HIFLUX] High Pressure Tube Support Catalog 2025](https://cdn.slidesharecdn.com/ss_thumbnails/tubesupporten-250529073613-16c22974-thumbnail.jpg?width=560&fit=bounds)
![[HIFLUX] Lok Fitting&Valve Catalog 2025 (Eng)](https://cdn.slidesharecdn.com/ss_thumbnails/lokfittingen-250528072439-8696f1c6-thumbnail.jpg?width=560&fit=bounds)
Lecture Note DL&NN Tensorflow.pptx
- 1. Deep Learning & Neural Network Tensorflow 1
- 2. 2 What is Tensorflow • TensorFlow is an open-source end-to-end platform for creating Machine Learning applications. • It is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. • It allows developers to create machine learning applications using various tools, libraries, and community resources. • TensorFlow is a library developed by the Google Brain Team to accelerate machine learning and deep neural network research. • It was built to run on multiple CPUs or GPUs and even mobile operating systems, and it has several wrappers in several languages like Python, C++ or Java
- 3. 3 Tensorflow Example • Currently, the most famous deep learning library in the world is Google’s TensorFlow. • Google product uses machine learning in all of its products to improve the search engine, translation, image captioning or recommendations.
- 4. 4 How Tensorflow works • TensorFlow enables you to build dataflow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. • It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.
- 6. 6 Tensorflow Architecture • Tensorflow architecture works in three parts: 1. Preprocessing the data 2. Build the model 3. Train and estimate the model • It is called Tensorflow because it takes input as a multi- dimensional array, also known as tensors. • You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. • The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output. • This is why it is called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.
- 7. 7 Tensorflow Components • Tensors. • Graph
- 8. 8 Tensorflow Components Tensors. • In Tensorflow, all the computations involve tensors. • A tensor is a vector or matrix of n-dimensions that represents all types of data. • All values in a tensor hold identical data type with a known (or partially known) shape. The shape of the data is the dimensionality of the matrix or array. • A tensor can be originated from the input data or the result of a computation. • In TensorFlow, all the operations are conducted inside a graph. • The graph is a set of computation that takes place successively. Each operation is called an op node and are connected to each other. • The graph outlines the ops and connections between the nodes. However, it does not display the values. The edge of the nodes is the tensor, i.e., a way to populate the operation with data.
- 9. 9 Tensorflow Components Graphs. • TensorFlow makes use of a graph framework. • The graph gathers and describes all the series computations done during the training. • The graph has lots of advantages: 1. It was done to run on multiple CPUs or GPUs and even mobile operating system 2. The portability of the graph allows to preserve the computations for immediate or later use. The graph can be saved to be executed in the future. 3. All the computations in the graph are done by connecting tensors together. A tensor has a node and an edge. The node carries the mathematical operation and produces an endpoints outputs. The edges the edges explain the input/output relationships between nodes.
- 10. 10 Tensor
- 11. 11 Tensor • Tensor: A tensor is a mathematical object which is a standard way of representing data in deep learning. Tensors are defined as a multidimensional array or as a list. Tensors are identified by the following parameters. • Rank: The rank of a tensor is defined by the number of directions required to describe a tensor. Example: A zero rank tensor is a scalar, a first rank tensor is a vector, a second rank tensor is typically a square matrix, etc. • Shape: Number of rows and columns of a tensor. • Type: Data type assigned to the tensor’s elements. Example: integer, float, string, or Boolean. • Flow(Graph): Flow is the graphical representation of mathematical operations. TensorFlow performs computations with the help of dataflow graphs. It has nodes that represent the operations in your model. We then write code to build the graph, create a session, and execute that graph.
- 14. 14 Competition
- 15. 15 Feature of Tensorflow 2.0
- 19. 19 Eager Execution - setup
- 20. 20 TF2 is imperative by default
- 21. 21 TF1 : Build a graph then run it • Build a graph (Construction Phase) • Run it (Execution Phase)
- 22. 22 Why is Tensorflow Popular? • TensorFlow is the best library of all because it is built to be accessible for everyone. • Tensorflow library incorporates different API to built at scale deep learning architecture like CNN or RNN. • TensorFlow is based on graph computation; it allows the developer to visualize the construction of the neural network with Tensorboad. This tool is helpful to debug the program. • • Finally, Tensorflow is built to be deployed at scale. It runs on CPU and GPU.
- 23. 23