Published on March 9, 2018

The course titled, “**Complete Guide to TensorFlow for Deep Learning with Python**” tells why the TensorFlow framework is very efficient in solving some of the trickiest problems. Some of the best companies in the world use this cutting-edge technology to leverage their sales by making statements out of the complex data by devising tools like these. It is also a fact that one can master the concepts of Machine Learning by taking up this course. If one needs to learn about Libraries that are used in deep learning with Python, they need not worry as those are also covered in this course.

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Udemy is a massive open online course (MOOC), provider. The platform has over 55,000 courses in various disciplines. The courses are taught by professors from elite universities all over the world and professionals working in huge established firms and those who have carved a niche for themselves in their respective areas of teaching. There are specialized education learning portals for few areas.

An all-in-one learning at the best quality is a rare find these days. The number of free courses that are offered in Udemy cannot be found elsewhere. The certificates from Udemy increases one’s resume value as the recruiters are well aware of the fact that Udemy only imparts the current needs using some of the best approach methods.

The interactive sessions by the course instructors make the learning even more interesting. The course instructors make sure that the course is taken in a very interesting manner. One can take up some of the best courses at the cheapest rates from the best teaching faculty. There are other MOOCs’ that offer courses, but the number of free courses that are offered by Udemy is so high when compared to the other MOOCs.’ IT sector is a field that requires constant learning; some full-time professionals work at IT firms take up courses in Udemy.

On having a look at the title, one might seem very perplexed and confused on what the author is trying to say. The following details will help even those who are not very well versed with the terms used in the computing to understand very easily. First of all,

Just like how, AngularJS, Node JS are frameworks for the web development languages, the TensorFlow is the framework for Deep Learning from Google. Many such frameworks from Google have made waves in the field of Artificial Intelligence and Data Science like the Map-Reduce framework of Machine Learning from google.

This Complete Guide to TensorFlow for Deep Learning with Python course tells one why the usage of this particular framework proves to be worthy and efficient to solve certain complex problems for Deep Learning using Python.

**When there are so many frameworks that are developed for Deep learning, why learn TensorFlow?**

The TensorFlow is an open source software that has its roots in the Graph Theory of Computer Science. It is flexible on all devices due to its simple architecture and API. Cycles, Trails, Hamiltonian Graphs, Eulerian Graphs, Paths are some of the terminologies that are used in the Graph Theory that is the theoretical basis for computer science. This framework was used by the Google Brain Team themselves for the Machine Learning, and the Artificial Intelligence needs. This is widely used by many reputed companies all over the world.

Deep Learning is the next stage of Artificial Neural Networks. It is the algorithm base for solving complex problems.

*Why should one take up this course for sure?*

__There are many reasons why one should take up this course. Few reasons are stated below:__

**Highly Updated Content**

The courses on Udemy.com are constantly updated, and they are always made sure that they cater to the needs of those who are not new and already know the current trends in the industry.

**Excellent teaching methodology**

The instructors of the course are highly qualified professionals who have been acquainted with the technology for at least a decade. The User Interface and the learning videos are designed in such a manner that the viewers keep coming back to register for new courses from Udemy.

**Trending Technology**

The Tensor Flow is the framework designed for Deep Learning which is an integral part of AI systems. Mastering an ongoing trend is not an easy task.

**What are the pre-requisites?**

This particular course in itself is here to help the candidate learn from scratch. Therefore a lot of pre-requisites is not placed before the candidate. A decent knowledge of the Python language is appreciated. For those who are not familiar with the basics of Python, there are so many courses that are available on Udemy.com for free itself. After equipping one with those basics, one can learn these

**Who is the target audience?**

In a nutshell,

- Complete beginners who want to learn from scratch
- The intermediate learners familiar with the basic terms and wish to get a broader and deeper understanding of the topic
- The ones who already mastered many of the concepts and are willing to learn what they had missed out learning and to implement the current trends

**To explain elaborately, the following section of people is the main target audience:**

- Anyone who is interested in learning about Deep Learning
- Anyone with good mathematical skills and some basic coding skills can take up this course
- Anyone who is willing and interested in pursuing a career in the field of Data Science can take up this course.
- Those who are already in the field and wish to learn and explore more can take up this course.

**What does one gain out of this Deep Learning Techniques course?**

There are many learning curves that one can obtain from this course. Few of which are given below:

- Understanding of working with Neural Networks
- How one can build their own Neural Network from scratch
- How to use sensor flow to perform things like, time series analysis using Recurrent Neural Networks, for Image Classification using Convolutional Neural Networks, Solving Unsupervised Learning Problems with autoencoders
- Learning Reinforcement Learning using OpenAI gym.
- To perform Classification and Regression Tasks
- Creating Generative Adversarial Networks using TensorFlow

**Topics covered in this TensorFlow course**

For few, many of the terms that are mentioned under what one gains from this Complete Guide to TensorFlow for Deep Learning with Python course might seem alien. A very brief explanation of the terms is given below.

**Machine Learning**

Learning is the ability to improve one’s behavior based on experience and to build computer systems that automatically improve with experience. Certain fundamental laws govern all learning processes. Machine Learning explores algorithms that can learn from data / build a model from data use the model for prediction, decision making or solving some tasks

Broad types of machine learning are

- Supervised Learning
- Unsupervised learning
- Semi-supervised Learning
- Reinforcement Learning

**To explain linear regression:**

Given an input x to compute an output y

For example, to predict height from age, To Predict house price from house area And to Predict distance from the wall from sensors.

A decision tree is a classifier in the form of a tree structure with two types of nodes:

*Decision node*: Specifies a choice or test of some attribute, with one branch for each outcome

*Leaf node*: Indicates classification of an example

**Neural Networks**

The following are some of the features of Neural Networks.

- Inspired by the human brain.
- Some NNs are models of biological neural networks
- The human brain contains a massively interconnected net of 1010-1011 (10 billion) neurons (cortical cells)
- Massive parallelism – a large number of simple processing units
- Connectionism – highly interconnected
- Associative distributed memory
- Pattern and strength of synaptic connections

Artificial Neural Networks (ANN)s incorporate the two fundamental components of biological neural nets namely Nodes – Neurons and Weights – Synapses

**Why Artificial Neural Networks to Deep Learning**

- Can represent interactions among inputs
- Two-layer networks can represent any Boolean function, and continuous functions (within a tolerance) as long as the number of hidden units is sufficient and appropriate activation functions used
- Learning algorithms exist, but weaker guarantees than perceptron learning algorithms

Breakthrough results in Image classification, Speech Recognition, Machine Translation and Multi-modal learning. Training networks with many hidden layers don’t work very well due to reasons like local minima and very slow training if initialize with zero weights and due to the diffusion of the gradient.

Feedforward NN is the Stacked Autoencoders (multilayer neural net with target output = input) and Stacked restricted Boltzmann machine and the Convolutional Neural Network

**Autoencoders and sparsity**

One has to place constraints on the network, like limiting the number of hidden units, to discover interesting structure about the data and impose sparsity constraint.

A neuron is “active” if its output value is close to 1. It is “inactive” if its output value is close to 0 with the constraint that the neurons to be inactive most of the time.

**Stacked Auto-Encoders**

One has to do supervised training on the last layer using final features and then do supervised training on the entire network to fine- tune all weights.

**Convolutional Neural Network**

Convolutional layers consist of a rectangular grid of neurons where each neuron takes inputs from a rectangular section of the previous layer, and the weights for this rectangular section are the same for each neuron in the convolutional layer. A CNN consists of some convolutional and subsampling layers.

The input to a convolutional layer is a m x m x r image where m x m is the height and width of the image and r is the number of channels, e.g., an RGB image has r=3, and the Convolutional layer will have k filters (or kernels) and the size n x n x q where n is smaller than the dimension of the image and q can either be the same as the number of channels r or smaller and may vary for each kernel.

Pooling is done using features obtained after Convolution for Classification. The pooling layer takes small rectangular blocks from the convolutional layer and subsamples it to produce a single output from that block like max, average, etc.

**The following are some of the properties of CNN**

- CNN takes advantage of the sub-structure of the input.
- It is achieved with local connections and tied weights followed by some form of pooling which results in translation invariant features.
- CNN are easier to train and have many fewer parameters than fully connected networks with the same number of hidden units.

**Reinforcement Learning**

A trial-and-error learning paradigm by learning about a system through interaction that is inspired by behavioral psychology. The following details are to be kept in mind about Reinforcement Learning.

- No detailed supervision available
- Trial-and-error learning
- Delayed rewards
- The sequence of actions required to obtain a reward
- Associative e-learning required
- The Need to associate actions to states
- Learning about policies and not just actions

**Salient Features of the course:**

The following are some of the salient features of the Complete Guide to TensorFlow for Deep Learning with Python course:

- 14 hours of interrupted video lectures
- Certificate provided on the completion of a course that includes listening and recording of the video lectures and taking the assignments that are made available on the portal.
- Supplementary study material apart from the video lectures that help the candidate to learn more about this concept.
- Enhances anytime, anywhere learning. Can be read on any digital electronic device with internet connection with a specified minimum amount of RAM capacity and the speed of the processor system used. The android phones, the electronic devices can be used.
- The course once bought by paying a certain amount can be accessed for a lifetime. The course never gets expired or doesn’t throw a situation where the user isn’t able to access the lecture videos anymore.

These are few of the many reasons to state why this Complete Guide to TensorFlow for Deep Learning with Python course is worth the penny spent. Moreover, it is one of the frameworks that are being used by the tech giant themselves, and this certification speaks volumes when one goes for a job interview or even for those who are wishing to climb their career ladder.

**More top-rated development courses:**

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Python for Data Science and Machine Learning Bootcamp

Machine Learning A-Z™: Hands-On Python & R In Data Science

Data Science A-Z™: Real-Life Data Science Exercises Included

Artificial Intelligence A-Z™: Learn How To Build An AI

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