Deep Learning Course

About Deep Learning Online Training Course

Deep Learning Online training course with TensorFlow provided by Besant Technologies will enclose the concepts of Artificial Intelligence, Deep learning frameworks, exploring neural networks, and implementation of machine learning algorithms with Deep networks. This real-time training will also examine you to learn different layers in neural networks and data abstraction features in Deep learning. Our hands-on Deep learning online training course is a stepping stone in your journey towards Data Science.

This placement oriented training ensures you to get good knowledge with real-time project support. We have the best trainers who are certified in Deep learning. Our trainers give you the best online training along with the lab practices in all trending Deep learning tools. Deep Learning advancements can be seen in creating power grid efficiency, smartphone applications, improving agricultural yields, advancements in healthcare, and finding climate change. With the help of this Deep Learning online course, one can know how to manage neural networks and interpret the results. The average salary for engineers with Deep learning skills is about $120,000 per annum.

What is TensorFlow?

TensorFlow is one of the Deep learning libraries developed by Google, which uses Machine learning in its products to improve the translation, search engine, and image captioning. TensorFlow takes input as a multidimensional array and constructs flowchart operations that are going to perform on the input. Google users can experience a more refined search with Artificial Intelligence.

If the user types a word in the search bar, Google displays the recommendations, what could be the next word. Google also uses Machine learning to take advantage of considerable data sets to give the best experience for users. Google doesn’t have massive data; that’s why it developed TensorFlow to accelerate Machine learning, Deep learning, and neural network research.

Example:

TensorFlow can be used to make algorithms in visualizing the objects in a photograph. It can also make the computer recognize the image objects.

Deep learning with TensorFlow

TensorFlow is a software library for Deep learning which is used for mathematical computation. It uses data flow graphs to represent multidimensional data arrays and mathematical operations. Google created it for Machine learning, but it is widely used for Deep Learning in developing solutions. Initially, load data on traffic signs and explore it with simple plotting and statistics. In this exploration process, the user needs to manipulate the data in such a way that it is possible to feed the data into the required model. That’s why it takes a lot of time to rescale images.

Example:

The best application of Deep Learning with TensorFlow is pattern recognition. It’s been applied to video, images, voice, sound, time-series data, and text. It classifies the data with human accuracy. It can also pick your application: turns as a lie detector by recognizing facial expressions.

Applications:

• Video Recognition
• Text Summarization
• Speech Recognition Systems
• Sentiment Analysis
• Self-Driving Cars

Deep learning with python:

Deep Learning is considered as one of the powerful machine learning techniques. Python is a high-level programming language that can be used for producing algorithms for Deep learning. Deep learning libraries are available on the Python ecosystem, such as TensorFlow, Keras, and Theano. Nowadays, the primary Python scripting skills are enough to do advance research in Deep Learning. Python libraries can provide functions in building deep nets that can train quickly on the machines. Let’s evaluate the mathematical expressions with matrices and vectors. Technically speaking, both the input data and neural sets are represented as matrices. In this, the developer can use a single machine with GPU. But, Python allows the implementation of vectorized functions.

Example:

By using Deep Learning, we can zoom the videos beyond the resolution. In the year 2017, Google researchers trained in Deep Learning and predicted faces in low-resolution images. The resolution of pixel recursive works on photos to expand to a great extent.

Applications:

• Pixel Restoration
• Describing Pictures
• Changing Gaze in Photos
• Real-Time Analysis of human behavior
• Translation
• Generating Pictures of Volcanoes and Galaxies
• Creating New Images
• Searching for Text in Videos and Images
• Generating Voice

What are neural networks?

Neural networks are algorithm sets that are modeled after the human brain to recognize patterns. Neural networks interpret the sensory data through clustering and labeling raw input and machine perception. The designs of Neural networks are numerical and contained in vectors. They can also clarify the clusters. Mostly, neural networks include computational unit layers with different connections; those layers are called neurons. These networks can able to transform the data to classify as an output. Each neuron multiplies the initial value by sum results or some benefits into the same neuron. Adjusting of the resulting number can be made by neuron’s bias and normalize the activation function output.

Example:

A neural network called DeepText can understand the textual content of thousands of posts within a fraction of seconds. This application can be used on Facebook.

Applications:

• Text classification
• Named Entity Recognition (NER)
• Semantic Parsing
• Part-of-Speech Tagging
• Paraphrase Detection
• Multi-document Summarization
• Speech Recognition
• Spell Checking
• Character Recognition
• Machine Translation

Natural Language Processing (NLP):

Natural language processing is a subfield of computer science, artificial intelligence, and information engineering concerned with the relation between human (natural) languages and computer languages. NLP also teaches the machines to perform the tasks of natural languages such as dialogue and machine translation. For a long time, the majority of problems to study NLP problems are time-consuming, shallow machine learning models, and hand-crafted features. Natural Language Processing separates the words into morphemes and identifies the class of every individual morpheme. This task difficulty depends on the structure of words being considered. The English language has simple morphology, which leads to the possibility of separating the words quickly. Such an approach is not possible in languages like Meitei and Turkish.

Example:

Natural language processing represents the observed text history with succinctly to predict the next word. Then it puts the predicted words in a sequence. This whole process is known as Language Modeling.

Applications:

• Language Modeling
• Speech Recognition
• Text Classification
• Caption Generation
• Document Summarization
• Machine Translation
• Question Answering

Deep Learning Training Course Syllabus

Introduction to Deep learning:

Objective: In this module, you will get a basic understanding of deep learning and what kind of problems deep learning will address. Also, get clarity about the difference between Machine learning and Deep learning.

• Origin of Deep Learning
• Machine Learning limitations
• Introduction about Deep Learning
• Deep Learning advantages and Machine Learning limitations
• Real-life use cases
• Brush up Machine Learning concepts

Hands-on: How to implement linear regression and logistic regression using Deep learning

Understanding the Neural network with TensorFlow:

Objective: In this module, you will get an idea about the deep learning structure and how we can build it using Neurons (Perceptron). You will familiarize yourself with different activation functions and have a brief introduction to the TensorFlow framework.

• Structure and working of Deep Learning
• Detailed explanation about Perceptron
• Different Activation functions
• Introduction to TensorFlow
• What is the computational graph?
• Basic TensorFlow coding and graph visualization
• Brief introduction about Variables, Constants and Place Holders
• Creating a simple TensorFlow model

Hands-on: Build a classification model using TensorFlow

Deep dive into Neural Network with TensorFlow:

Objective: In this module, you will be learning about Deep Neural Networks and how does it work. Also, you will get more understanding of forwarding and backward propagation.

• Different layers in the Neural network
• Understanding Neural Network in detail
• Introduction to Multi-layer Perceptron
• What is Forward propagation and Backpropagation?
• Build a Multi-layer perceptron model using TensorFlow
• Familiarise in using Tensor Board

Hands-on: Build a deep neural network to classify digits in MNIST dataset

Master Deep Networks:

Objective: In this module, you will get more hands-on for the TensorFlow framework. You will get to know more details about the data flow in TensorFlow.

• What is a Deep Neural Network?
• How Deep Neural Network helps to increase accuracy?
• Understanding the working of Deep Neural Network
• What is Weight and Bias, how it is getting updated?
• How gradient descent is useful to update parameters?
• Types of Deep Networks

Hands-on: Building a classification model using a Multi-layered perceptron.

Convolutional Neural Network(CNN):

Objective: In this module, you will be learning what convolutional neural network is and how it is different from Feedforward neural network. Also, you will get an understanding of various layers in the Convolutional Neural Network and what are the real-time applications of CNN.

• Introduction to CNN
• Advantage of CNN over other Neural Networks
• Applications of CNN
• Architecture of CNN
• Different layers and its use to build a CNN model
• Real-time use cases of CNN

Hands-on: Building an Image classification model using CNN

Recurrent Neural Network (RNN):

Objective: In this module, you will get an understanding of what is RNN and it’s working. Also, get to know the advantage of RNN over other Neural Network models and you will be familiarised with LSTM, why we need to use LSTM and the real-time application of LSTM.

• Introduction to RNN
• How RNN is different from other Neural Network models
• Structure and working of RNN
• Exploding and Vanishing Gradient descend problem
• Long Short-Term Memory (LSTM)
• How LSTM overcome the problem of Vanishing Gradient descent?
• Real-time use-cases of LSTM

Hands-on: Building an RNN model to predict the next word in the sentence.

Restricted Boltzmann Machine (RBM) and Autoencoders:

Objective: In this module, you will get a great understanding of RBM and Autoencoders. Also, you will get an idea about how Autoencoders different from PCA and you will be gone through some real-time applications of RBM and Autoencoders.

• What is Restricted Boltzmann Machine (RBM)
• Applications of RBM
• How to do Collaborative Filtering with RBM?
• Introduction to Autoencoders
• Autoencoders applications
• Understanding Autoencoders and how it is different from PCA

Hands-on: Predicting the customer rating for each movie.

Keras API:

Objective: In this module, you will get to know the various functions and features of Keras API and how does it use. After this session, you will be able to develop a Neural Network model using Keras API.

• Introduction to Keras
• How to build a Models in Keras using TensorFlow backend
• Sequential and Functional Composition
• Explaining Predefined Neural Network Layers
• What is Batch Normalization?
• How to save and load a model
• Using TensorBoard with Keras

Hands-on: Building an image classification model using Keras

TFLearn API:

Objective: In this module, you will get to know the different functionalities and features of TFLearn API and how does it use. After this session, you will be able to develop a Neural Network model using TFLearn API.

• Introduction to TFLearn
• How to build a Models in TFLearn using TensorFlow backend
• Sequential and Functional Composition
• Explaining Predefined Neural Network Layers
• What is Batch Normalization?
• How to save and load a model
• Using TensorBoard with TFLearn

Hands-on: Building a Neural network model to classify the digits in MNIST dataset using TFLearn

Upcoming Batch Schedule for Deep Learning Online Training

Besant Technologies provides flexible timings to all our students. Here is the Deep Learning Online Training Schedule for our branch. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

• 25-11-2024 Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fees
• 21-11-2024 Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fees
• 23-11-2024 Sat (Sat - Sun)Weekend Batch 11:00 AM (IST) (Class 3Hrs) / Per Session Get Fees

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Trainer Profile of Deep Learning Online Training

Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.

• More than 7+ Years of Experience.
• Trained more than 2000+ students in a year.
• Strong Theoretical & Practical Knowledge.
• Certified Professionals with High Grade.
• Well connected with Hiring HRs in multinational companies.
• Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
• Trainers have Experienced on multiple real-time projects in their Industries.
• Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc

Deep Learning Exams & Certification

Besant Technologies Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.

Our certification at Besant Technologies is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.

Deep Learning Certification Training Key Features:

Projects of Deep Learning Online Training

Project title – CNN based face detector from dlib

Industry – Artificial Intelligence

Problem Statement – Detect the face in all angles with the highest Accuracy.

Topics – This detector is based on the histogram of oriented gradients (HOG) and linear SVM. Explaining how

these detector works are beyond the scope of this project.

Highlights

I have majorly used dlib for face detection and facial landmark detection. The frontal face detector in dlib works really well. It is simple and just works out of the box.

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