Deep Learning with TensorFlow

Introduction on Deep Learning with TensorFlow

Deep Learning in TensorFlow has garnered a lot of attention from the past few years. Deep Learning is the subset of Artificial Intelligence (AI) and it mimics the neuron of the human brain. Deep Learning Models create a network that is similar to the biological nervous system. It imitates the human thinking process. Deep learning is the collection of nodes where each node acts as a neuron. The hype about deep learning is pretty dense.

Why we need this?

The reasons why we need Deep Learning Models are to solve complex problems like speech detection, Image recognition, etc. In terms of accuracy, Deep learning models are best when they are trained with huge amounts of data. If you feed Deep learning models with unlabeled and unstructured data it still manages to draw insights from the data. Walter Pitts and Warren McCulloch created the first computer model based on the neural networks which mimic the human brain late back in the year 1943. The Decision of the deep learning models is based on the input to the nodes. This Neural Network has Input Layers, Hidden Layers, and Output Layers. The Input is feed to the Model through Input Layers, Hidden layers perform the processing and the output is generated at the output layers.

Importance of Deep Learning with TensorFlow

“The Deep Learning Market will grow to reach a value of $1,772.9 million by 2022 “ is anticipated by MarketsandMarkets. It is easy to build a Neural network i.e. Deep Learning models without having to worry about the underlying algorithm with the help of an interface, library, and tool known as Deep Learning Framework. Different Deep Learning Frameworks are:

• Caffee
• Keras
• Microsoft Cognitive Toolkit
• MXNET
• DeepLearning4j
• Chainer
• TensorFlow

Different Frameworks are builds for different purposes. But among All these Frameworks TensorFlow is one of the highly flexible system Architecture. Many Big giants like Facebook, Google, Deepmind, Nvidia, Airbnb, Lenovo, etc. are now adopting TensorFlow. The Top applications of the TensorFlow are Speech Recognition Systems Autonomous cars, Summarization of Text, Sentiment Analysis, Image recognition, Video Recognition, Tagging, Handwriting recognition, Forecasting. TensorFlow supports Programming Languages such as python, R and C++ and available on both mobile and Desktop.

TensorFlow Builds Models using Data Flow Graphs it is an open-source Artificial Intelligence Library. Why it is called TensorFlow? It works on Tensors (n-dimensional array) and flows (as data goes in and after processing comes out of the network). Each node in the neural network is a mathematical operation and edges represent data array (n-dimensional). TensorFlow was released by Google in the year 2015. TensorFlow is a powerful library for implementing big machine learning and deep learning projects.

Steps of TensorFlow Algorithm

The basic steps of TensorFlow algorithm are:

Step 1: Data is Imported/Generated: TensorFlow Models depends heavily on the huge amount of Data. Either you can import your own dataset or TensorFlow also comes with the collection of datasets ready to use.

• Type this command to check out available datasets in TensorFlow

import TensorFlow as tf
import TensorFlow_datasets as tendata
#This command will generate a list of datasets available in the TensorFlow
print(tfds.list_builders())

Step 2: Data Normalization or Transformation: If the data is not in the appropriate forum. The Batch Normalization is the command approach used to normalize data in the TensorFlow.

Step 3: Set the Parameters of the Algorithm: For eg; the number of Iterations, Learning rate, etc.

Step 4: Set and initialize the variables and Placeholders: Variables and Placeholders are two basic programming Elements of the TensorFlow. Variables hold the state of the graph and placeholders are used to feed the data in the graph at the later date.

Step 5: Create Model structure: What operations will be performed on the data is defined.

Step 6: Define the Loss Function: It calculates the difference between predicted values and actual values. It tells how well your model is trained basically used to evaluate the output.

Step 7: Train Model: Initialize computational graph and create an Instance of a graph. Feed data into the model with the help of placeholders and let the TensorFlow do the rest of the processing for better predictions.

Step 8: Evaluate the performance: Evaluate the model by checking with new data.

Step 9: Predict the Outcome: Also checks your model on new and unseen data.

To better visualize model TensorFlow provides Tensorboard. It helps us to visualize any statistics of the neural network, debug and optimize them. You can check what happens in the code and will give you a detailed understanding of the inner working. You can fix problems very easily with the help of this tool. Tensorboard provides five types of Visualizations:

• Scalars
• Images
• Audio
• Histograms
• Graphs

The summary function of the TensorFlows gives us a detailed summary according to the specified format. To allocate resources, hold intermediate results and variables and execute graphs or part of graph session function is used.

Advantages

• It is an Open source and Free to use.
• It is Backed by big giant google so frequent releases of software, quick updates, and good performance is achieved.
• It Provides Tensorboard which allows us to visualize graphs and see the inner working.
• Without any hardware support, you can perform high-level computation on your laptop. TensorFlow runs on CPU, GPU, desktop, servers, and mobile platforms. They are scalable as well.
• Debugging the subpart of the Graph is easy using TensorFlow.

Scope in Deep Learning in TensorFlow

Deep Learning is creating a huge impact on our lives. The skills required to start your career in deep learning are Modelling Deep learning neural networks like CNN, RNN, LSTM, ADAM, Dropout, etc. and a good understanding of the probabilistic methods. The average salary of machine Learning engineers according to Glassdoor is 8LPA. The Average salary for deep learning engineers will be high as compared to machine learning Engineers. TensorFlow Provides Excellent functionalities compared to deep learning library. As big tech giants are adopting this technology so the need for TensorFlow experts has already been raised in the market.

Conclusion

In this article, we discussed the basics of TensorFlow its advantages and applications. Keeping in mind the above facts about TensorFlow it can be said that if you have a strong desire to learn AI kick start your career now.

Recommended Articles

This is a guide to Deep Learning with TensorFlow. Here we discuss the introduction, Importance of Deep Learning with TensorFlow, Scopes, advantages, and Steps in the algorithm. You can also go through our other related articles to learn more–

1. Perceptron Learning Algorithm
2. Tensorflow Image Classification
3. Careers in Deep Learnings
4. Deep Learning Frameworks
5. Normalization in DBMS | How to Work?
6. Guide to Deep Learning Toolbox