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