What is TensorFlow?
TensorFlow is an open source software library to build deep learning models.
Google’s TensorFlow is the most famous deep learning library in terms of ease of use, accessibility, effectiveness and scale of production.
The cross platform framework is Python friendly and runs on multiple CPUs and GPUs including mobile and embedded platforms.
What is TensorFlow in deep learning?
Compared to other deep learning framework, it has the largest repositories in GitHub.
Such is its popularity and why is that?
Firstly, TensorFlow excels at numerical computing.
Secondly, it provides API in most languages and environments used in deep learning projects.
Thirdly, TensorFlow enables machine learning to build complex application with great accuracy.
Being a computational framework, it allows developers to build and deploy applications with high performance.
It provides a variety of different toolkits to allow you to construct deep learning models at your preferred level of abstraction.
What is TensorFlow used for?
It was mainly developed for running large data sets of numerical computations.
It uses data flow graphs to process data and performs computations.
TensorFlow facilitates easier computation and analysis of neural networks by using multi-dimensional array called Tensors (like NumPy) and by computing these graphs in Sessions.
Currently, it is widely used in voice recognition, image recognition, video detection and almost all major big data companies right now use TensorFlow for Deep learning.
What is TensorFlow Keras?
Written in Python, Keras is a high neural network API that is built on top of TensorFlow’s low level API.
It allows Python ML developers and researchers to design, experiment neural networks a lot easier.
What is TensorFlow API?
TensorFlow Courses and Tutorials
Below is our pick of best TensorFlow courses, best TensorFlow tutorials and best TensorFlow books to learn online.
This is hands down the best resource you can find to learn deep learning fundamentals with TensorFlow.
The Coursera deep learning specialization has 5 courses in total and will take an approx 3 months to completely master deep learning and start a career in artificial intelligence.
The best part, the course material is coming from an author who founded and led the ‘Google Brain Project’ in developing deep learning algorithms.
What you will learn from the TensorFlow training course: Deep Learning foundations, how to build neural networks and lead Machine learning projects, what are CNN, RNN, LSTM, Adam, Dropout, BatchNorm, Xavier followed with case studies from various sectors.
Once you successfully complete all the 5 courses and complete the hands on project, you will earn a course completion certificate.
Course 1: Neural Networks and Deep Learning
Course 3: Structuring Machine Learning Projects
Course 4: Convolutional Neural Networks
Course 5: Sequence Models
The machine learning with TensorFlow specialization has 5 courses in total and talks about what Google thinks about ML, history of ML, role of neural networks in data science problems, how to create machine learning models in TensorFlow, feature engineering on GCP and more.
Course 1: How Google Does Machine Leaning?
Course 2: Launching into Machine Learning
Course 3: Intro to TensorFlow
Course 4: Feature Engineering
Course 5: Art and Science of Machine Learning
You can subscribe to the entire specialization or study individual courses depending on your choice of learning.
In order to earn a certificate, you have to complete the hands-on project associated with it.
This is the most popular and best selling TensorFlow course if you want to learn from the Udemy platform.
The course has already seen 50,000 pus enrollments and is created by Udemy’s popular data scientist Jose Portilla.
What you will learn:
- Understand how neural networks work
- How to use TensorFlow for Classification and Regression Tasks-
- TensorFlow with RNN
- Conduct Reinforcement learning with OpenAI Gym
- Build Neural network with Python
- TensorFlow with CNN for image classification
- TensorFlow for solving Unsupervised learning
- Create GAN with TensorFlow
One of the great ways to learn TensorFlow is from Google itself.
The TensorFlow crash course has an intensive and practical 20 hour intro to ML fundamentals and concepts using TensorFlow excercises.
Part of their Learn with Google Initiative, the Google TensorFlow course talks about basic algorithms, classification models and how neural networks run.
The TensorFlow pdf gives you a good introduction to TensorFlow and the author explains the concepts in a clear and precise way with easy to understand examples.
This popular deep learning with TensorFlow at Edureka helps you master concepts like SoftMax function, Anutoencoder Neural Networks, CNN, RNN, RBM, etc and work with libraries like Keras and TF Learn.
All these courses are instructor led with live online classes, has in-class projects, real life case studies, assignments and comes with a certificate of completion.
It has indeed great reviews and testimonials from enrolled students. Make sure you check that before enrolling.
One of the other best places to start with TensorFlow is their official documentation page.
You could find all API and built in functions theoretical explanations with examples.
Go through all the tutorials for a thorough knowledge on TensorFlow.
Google launched deep learning course in collaboration with Udacity to help data scientists get started with deep learning and address some of the most common ML problems.
The Nanodegree program has interactive TensorFlow notebooks to implement concepts introduced in the lectures.
The course is designed for those ML enthusiasts who are looking to solve real and interesting problems with deep learning techniques.
Gives you a thorough introduction to ML tools and development environment and working experience with Python, NumPy and TensorFlow.
The course curriculum is divided into 4 major modules: Intro to neural networks, CNN, RNN and GAN.
Note: It is not a self paced course and have deadlines to finish assignments and projects.
The course runs for 3- 4 months, have a total of 5 projects to complete and interaction with deep learning experts at the community forum is a plus.
Simplilearn deep learning course is designed by subject matter experts (Mike Tamir and Vivek Singhal) in AI, ML and deep learning.
The course spans over 40 hours instructor led training sessions with real life based projects.
Upon completion of projects, you are awarded with deep learning TensorFlow certification.
Through this course, you will be able to master deep learning techniques and build deep learning models using TensorFlow.
This is IBM deep learning professional certification program at EDX.
You can learn as a Tensorflow free online course if you don’t want a verified certificate at the end.
Otherwise you have to pay $99 for the same.
What you will learn:
- Foundational TensorFlow Concepts
- How TensorFlow used in error fitting, regression classification and minimization of error functions
- Understand different neural architectures like CNN, RNN and Autoencoders
- How to apply TensorFlow for backpropagation
TopTal has nice blog posts to learn TensorFlow concepts at a beginner level.
The TensorFlow tutorial talks about how to use ML models in TensorFlow and how to utilize the library for debugging, visualizing and tweaking the model.
This is a beginner level free community tutorial on TensorFlow at DataCamp.
The TensorFlow tutorial is aimed at beginners looking for an interactive learning experience.
Learning aspects in the course includes: Tensors, TensorFlow installation process, TensorFlow basics, data manipulation with simple statistics and how to build your own neural network model.
Coming from the creators of Apache Spark, Databricks documentation can act as a deep learning guide and help you get started with TensorFlow.
You will learn how to install TensorFlow as a Databricks library, use tensorboard for debugging, optimising and understanding TensorFlow programs, use TensorFlow on a single node and spark TensorFlow connector for data conversion purpose.
An introductory tutorial on Tensorflow, it provides an overview of some of the basic concepts of TensorFlow in Python.
You are required to have some knowledge about neural networks in order to understand this tutorial.
This free TensorFlow tutorial gives you basic introduction to TensorFlow concepts such as knowing what are Tensors, Tensor Calculations, Computation Graph, Variables, etc.
TensorFlow Tutorial – Deep Learning Using TensorFlow – Edureka Blog
The TensorFlow tutorial blog at Edureka gives you a decent start to TensorFlow basics.
You learn about Tensors,write a TensorFlow Programs, computational graphs, what are Constants, Placeholder and Variables and how to implement a linear regression model using TF.
The TensorFlow free online course from Kadenze Academy has 5 sessions in total and covers basic components of deep learning with TensorFlow and touches advance neural network concepts like CNN, RNN, GAN, etc.
If you want to opt for a TensorFlow certification, you need to opt for their premium membership plan at $20 per month.
Tensorflow tutorials on GitHub:
Github repositories have some simple and comprehensive tutorials on TensorFlow from hard core developers.
A curated list of TensorFlow experiments, libraries and projects – jtoy/awesome-tensorflow
TensorFlow Tutorial from basic to hard – MorvanZhou/Tensorflow-Tutorial
TensorFlow tutorials and examples for beginners with Latest APIs – aymericdamien/Tensor-Flow-Examples
TensorFlow Tutorials with YouTube Videos – Hvass-Labs/TensorFlow-Tutorials
Simple Tutorials using Google’s TensorFlow Framework – nlintz/TensorFlow-Tutorials
TensorFlow Documentation – tensorflow/docs
A best practice for TensorFlow project template architecture – MrGemy95/TensorFlow-Project Template
YouTube Videos for Learning TensorFlow:
YouTube can be a good self studying medium to learn tensorflow for free.
Some of the best ones worth checking:
TensorFlow and deep learning – without a PhD by Martin Gorner
Best TensorFlow Books:
When it comes to TensorFlow books, these three books have some awesome information to understand TensorFlow
TensorFlow for Machine Learning: A hands-on introduction to learning algorithms
Let us know in the comments sections what are your best picks for learning TensorFlow?
Happy Data Learning!