Deep Learning Course (with Keras and TensorFlow)

Deep learning is known to be the most trending career path in this technologically advanced world of artificial intelligence and machine learning. With the latest development in the deep learning industry, this deep learning course with Keras and TensorFlow is designed and aligned with the help of industry experts. Therefore, in this course period, you'll learn to implement deep learning algorithms in Keras and TensorFlow frameworks.

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Deep Learning Course Online – Overview

If you want to gain a competitive edge and advance your career, then this deep learning training will be the ideal course for you. As part of the deep learning course certification training, you'll develop your skills in PyTorch, artificial neural networks, autoencoders, etc. Furthermore, with this deep learning course online, you will learn to build your deep learning project by building deep learning models and interpreting results.

Deep Learning Training Online – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.

Deep Learning Online Course – Benefits

With the increasing demand for artificial intelligence and its subsets – machine learning and deep learning, the global market for deep learning is expected to rise from $12.3 billion to $60.5 billion in the period 2020-2025, with a CAGR of 37.5%.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Deep Learning Engineer

UK
Hiring Companies

Deep Learning Course (with Keras and TensorFlow) – Training Options

Self-Paced Learning

£ 1200.00

  • 1-year access to the Deep Learning course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Access to the class recordings.
  • Assistance and support.
  • Download certification.
  • Free mentorship.

Online Boot Camp

£ 1000.00

  • Everything in Self-paced learning +
  • On-spot doubt clarification.
  • Interactive training sessions.
  • Sessions on the capstone project.
  • Live, online classroom training.
  • Mock-interviews.

Corporate Training

Customized to your team’s needs

  • 1-year access to the Deep Learning course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Class recordings.
  • Assistance and support.
  • Certification after the course.

Deep Learning Course Certification Training – Curriculum

Eligibility

As there is an increase in demand for deep learning engineers, this deep learning course (with Keras and TensorFlow) can be the ideal path for engineering graduates as well as intermediate level professionals who are seeking a career in deep learning, machine learning, or artificial intelligence domain. Therefore, software engineers, data analysts, python programmers and developers can join this deep learning course online.

Pre-requisites

Students or working professionals who want to join this deep learning training course online should be familiar with the programming concepts, understanding of mathematical and statistical concepts along with the foundational knowledge of machine learning.

Course Content

  • 1.1 Welcome!
  • 1.2 Learning Objectives
  • 2.1 Learning Objectives
  • 2.2 Introduction to TensorFlow
  • 2.3 TF2x and Eager Execution
  • 2.4 Tensorflow Hello World
  • 2.5 Linear Regression With Tensorflow
  • 2.6 Logistic Regression With Tensorflow
  • 2.7 Intro to Deep Learning
  • 2.8 Deep Neural Networks
  • 3.1 Learning Objectives
  • 3.2 Intro to Convolutional Networks
  • 3.3 CNN for Classifications
  • 3.4 CNN Architecture
  • 3.5 Understanding Convolutions
  • 3.6 CNN with MNIST Dataset
  • 4.1 Learning Objectives
  • 4.2 The Sequential Problem
  • 4.3 The RNN Model
  • 4.4 The LSTM Model
  • 4.5 LTSM Basics
  • 4.6 Applying RNNs to Language Modeling
  • 4.7 LSTM Language Modelling
  • 5.1 Learning Objectives
  • 5.2 Intro to RBMs
  • 5.3 Training RBMs
  • 5.4 RBM with MNIST
  • 6.1 Learning Objectives
  • 6.2 Intro to Autoencoders
  • 6.3 Autoencoder Structure
  • 6.4 Autoencoders
  • 7.1 Course Summary
  • Introduction
  • Accessing Practice Lab
  • What is AI and Deep learning
  • Brief History of AI
  • Recap: SL, UL and RL
  • Deep learning : successes last decade
  • Demo & discussion: Self driving car object detection
  • Applications of Deep learning
  • Challenges of Deep learning
  • Demo & discussion: Sentiment analysis using LSTM
  • Fullcycle of a deep learning project
  • Key Takeaways
  • Knowledge Check
  • Biological Neuron Vs Perceptron
  • Shallow neural network
  • Training a Perceptron
  • Demo code: Perceptron ( linear classification) (Assisted)
  • Backpropagation
  • Role of Activation functions & backpropagation
  • Demo code: Backpropagation (Assisted)
  • Demo code: Activation Function (Unassisted)
  • Optimization
  • Regularization
  • Dropout layer
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project (MNIST Image Classification)
  • Deep Neural Network : why and applications
  • Designing a Deep neural network
  • How to choose your loss function?
  • Tools for Deep learning models
  • Keras and its Elements
  • Demo Code: Build a deep learning model using Keras (Assisted)
  • Tensorflow and Its ecosystem
  • Demo Code: Build a deep learning model using Tensorflow (Assisted)
  • TFlearn
  • Pytorch and its elements
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset
  • Optimization algorithms
  • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
  • Batch normalization
  • Demo Code: Batch Normalization (Assisted)
  • Exploding and vanishing gradients
  • Hyperparameter tuning
  • Interpretability
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Hyperparameter Tunning With Keras Tuner
  • Success and history
  • CNN Network design and architecture
  • Demo code: CNN Image Classification (Assisted)
  • Deep convolutional models
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Image Classification
  • Sequence data
  • Sense of time
  • RNN introduction
  • LSTM ( retail sales dataset kaggle)
  • Demo code: Stock Price Prediction with LSTM (Assisted)
  • Demo code: Multiclass Classification using LSTM (Unassisted)
  • Demo code: Sentiment Analysis using LSTM (Assisted)
  • GRUs
  • LSTM Vs GRUs
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Stock Price Forecasting
  • Introduction to Autoencoders
  • Applications of Autoencoders
  • Autoencoder for anomaly detection
  • Demo code: Autoencoder model for MNIST data (Assisted)
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Anomaly detection with Keras
  • PUBG Players Finishing Placement Prediction

Deep Learning Training Course Online – FAQs

With the day-to-day lifestyle activities in this modern high-tech world, huge data is being collected by the companies that help them to draw meaningful insights into effective business operations. As the collected data is in the form of images, text, audio, and video categorized in an unstructured format, deep learning techniques enable you to analyze effectively and draw meaningful insights that are vital for business decisions.

In this technologically advancing era, people with deep learning skills are experiencing multiple jobs and career growth opportunities. Furthermore, some of the high demanded roles like machine learning engineer, data scientist, NLP engineer, and deep learning engineer require a high-level understanding of the concepts. And as self-learning will not be the viable option for better career growth, it is advisable to get through this deep learning course.

With all the knowledge and skills that you have acquired from this deep learning training course online, you can apply for job positions such as Deep Learning Engineer, Machine Learning Engineer, NLP Scientist, Data Scientist, Business Intelligence Developer, etc.

You can register for this online deep learning training course by filling in all your essential details and making a payment through digital modes. However, before enrolling for the course, it is suggested to check whether you qualify for the pre-requisites requirements for this course. However, once the payment is received, you will be contacted via email or phone to proceed with the further process.

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