TensorFlow is an open-source machine learning concept which is designed and developed by Google. It offers a really high level and abstract approach to organizing low-level numerical programming. And supporting libraries which will allow our software to run without changes on regular CPU.

It supported platforms include Linux, macOS, Windows, and Android.

TensorFlow models can also be run without a traditional computer platform in the Google Cloud Machine Learning Engine.

1.1

The more advanced technology, and therefore the more useful it is often, but everything has its downside and also this machine learning library. When comparing TensorFlow with other libraries like Torch, SciKit, Theano, Neon, there are drawbacks in several features that the library lets us manipulate. This library is meant and updated by Google, so needless to mention, and it’s come a far way since its initial release.

Advantages of TensorFlow

1.2

1) Graphs:

TensorFlow has better computational graph visualizations. Which are inherent in comparison to other libraries like Torch and Theano.

1.3

2) Library management:

Google backs it. And has the benefits of seamless performance, quick updates, and frequent new releases with new features.

3) Debugging:

It helps us execute subpart of a graph which gives it an upper hand as we can introduce and retrieve discrete data

4) Scalability:

The libraries are deployed on a hardware machine, which is a cellular device to the computer with a complex setup.

5) Pipelining:

TensorFlow is meant to use various backend software (GPUs, ASIC), etc. and also highly parallel.

6) It has a unique approach that permits monitoring the training progress of our models and tracking several metrics.

7) TensorFlow has excellent community support.

8) Its performance is high and matching the best in the industry.

Disadvantages of TensorFlow

1.4

1) Missing Symbolic loops:

When we say about the variable-length sequence, the feature is more required. Unfortunately, TensorFlow doesn’t offer functionality, but finite folding is that the right solution thereto.

2) No supports for windows:

There is a good sort of users who are comfortable during a window environment instead of Linux, and TensorFlow doesn’t satisfy these users. But we’d like not worry that if we are a window user we will also install it through conda or python package library (pip).

3) Benchmark tests:

TensorFlow lacks in both speed and usage when it’s compared to its competitors.

4) No GPU support for Nvidia and only language support:

Currently, the only supported GPUs are NVIDIA and therefore the only full language support of Python, which makes it a drawback as there’s a hike of other languages in deep learning as well as the Lau.

5) Computation Speed:

This is a field where TF is delaying behind, but we specialize in the assembly environment ratherish than the performance, it’s still the proper choice.

6) No support for OpenCL.

7) It requires fundamental knowledge of advanced calculus and linear algebra alongside good understanding of machine learning also.

8) TensorFlow has a unique structure, so it’s hard to find an error and difficult to debug.

9) There is no need for any super low-level matter.

10) It is a very low level with a steep learning curve.

So, this brings us to the end of blog. This Tecklearn ‘Advantages and Disadvantages of Tensor Flow’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/artificial-intelligence-and-deep-learning-with-tensorflow/

Artificial Intelligence and Deep Learning with TensorFlow Training

About the Course

Tecklearn’s Artificial Intelligence and Deep Learning with Tensor Flow course is curated by industry professionals as per the industry requirements & demands and aligned with the latest best practices. You’ll master convolutional neural networks (CNN), TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects. The trainee will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and more programming languages needed to shine in this field.

Why Should you take Artificial Intelligence and Deep Learning with Tensor Flow Training?

According to Paysa.com, an Artificial Intelligence Engineer earns an average of $171,715, ranging from $124,542 at the 25th percentile to $201,853 at the 75th percentile, with top earners earning more than $257,530.
Worldwide Spending on Artificial Intelligence Systems Will Be Nearly $98 Billion in 2023, According to New IDC Spending Guide at a GACR of 28.5%.
IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle and almost all the leading companies are working on Artificial Intelligence to innovate future technologies.

What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

Got a question for us? Please mention it in the comments section and we will get back to you.

1,004