Deep Learning

About Deep Learning

Processing a large amount of data for deep learning requires a large amount of computational capacity, and these deep learning approaches are used to create predictive software for fraud detection, click forecasting, demand forecasting, and other intensive analytics data. Students in the training program can acquire knowledge of theories such as neural networks, regression models, unsupervised learning, neural network models, and other advanced concepts. This advanced deep learning course in Pune is definitely the choice to master deep learning knowledge.

What is Deep Learning?

Deep learning is a special subset of machine learning. This technology teaches computers and machines to mimic human behavior and to properly learn from them. A successful deep learning program may require a significant amount of tagged data and massive computing power, but with a new study we can apply techniques like transfer learning and get cutting-edge results with even less information. This is one of the main reasons deep learning is so popular.

Why with Educora?

One of the reasons why Educora is one of the best institutes for deep learning in Pune is our proactive and practice-oriented training methods. Our program is project based, which can help you apply your insights in real time. The system will be the first of its kind in India. In addition, our face-to-face events take place in the evenings so that both students and professionals can take part. We also maintain a minimum number of trainees for each batch so that the trainers can offer individual tutoring for each student. Educora is one of the few institutes that offers deep learning courses with an internship in Pune. We assure you 100% assistance in finding some of the best companies in the domain. But you need to see that depending on your interview and project performance, positioning will be the first step in planning a career in deep learning and artificial intelligence.

Online Class

As we move into a world that requires higher levels of computing, this is where deep learning comes in, which is capable of handling a wide variety of functions, making it very powerful when you have to deal with data. Educora’s online deep learning training offers practical training in deep learning and accelerated computing and data science. With these online deep learning courses, students, developers, data scientists and analysts can gain hands-on experience with projects in live and receive a certificate that will support their professional growth. Start with Educora’s online deep learning courses to gain proficiency in deep learning. After completing the course with the Educora Job Assistance Program, you can be employed by multinational companies.

COURSE ELIGIBILITY

  • Freshers
  • BE/Bsc Candidate
  • Any Engineers
  • Any Graduate
  • Any Post-Graduate
  • Working Professionals

COURSES CONTENT​

  • Artificial Intelligence
  • An Introduction to Artificial Intelligence
  • History of Artificial Intelligence
  • Future and Market Trends in Artificial Intelligence
  • Smart Agents – Perceive-Reason-Act Loop
  • Search and Symbolic Search
  • Requirement based Reasoning
  • Straightforward Adversarial Search (Game-Playing)
  • Neural Networks and Perceptions
  • Understanding Feed forward Networks
  • Boltzmann Machines and Auto encoders
  • Investigating Back propagation
  • Deep Networks and Structured
  • Deep Networks/Deep Learning
  • Information based Reasoning
  • First-request Logic and Theorem
  • Rules and Rule-based Reasoning
  • Contemplating Blackboard Systems
  • Organized Knowledge: Frames, Cyc, Conceptual Dependency
  • Portrayal Logic
  • Dissuading Uncertainty
  • Likelihood and Certainty-Factors
  • What are Bayesian Networks?
  • Understanding Sensor Processing
  • Regular Language Processing
  • Contemplating Neural Elements
  • Convolutional Networks
  • Repetitive Networks
  • Long Short-T
  • AI and Hacking
  • AI
  • Repeat: Deep Learning
  • Representative Approaches and Multiagent Systems
  • Cultural/Ethical Concerns
  • Hacking and Ethical Concerns
  • Conduct and Hacking
  • Occupation Displacement and Societal Disruption
  • Morals of Deadly AIs
  • Risk of Displacement of Humanity
  • The eventual fate of Artificial Intelligence
  • Regular Language Processing
  • Normal Language Processing in Python
  • Normal Language Processing in R
  • Concentrating Deep Learning
  • Fake Neural Networks
  • ANN Intuition
  • Plan of Attack
  • Contemplating the Neuron
  • The Activation Function
  • Working of Neural Networks
  • Investigating Gradient Descent
  • Stochastic Gradient Descent
  • Investigating Backpropagation
  • Fake and Conventional Neural Network
  • Understanding Artificial Neural Network
  • Building an ANN
  • Building Problem Description
  • Assessment the ANN
  • Working on the ANN
  • Tuning the ANN
  • Regular Neural Networks
  • CNN Intuition
  • Convolution Operation
  • ReLU Layer
  • Pooling and Flattening
  • Full Connection
  • Softmax and Cross-Entropy
  • Building a CNN
  • Assessing the CNN
  • Working on the CNN
  • Tuning the CNN
  • Repetitive Neural Network
  • Repetitive Neural Network
  • RNN Intuition
  • The Vanishing Gradient Problem
  • LSTMs and LSTM Variations
  • Commonsense Intuition
  • Building a RNN
  • Assessing the RNN
  • Improving
  • Self-Organizing Maps
  • Self-Organizing Maps
  • SOMs Intuition
  • Plan of Attack
  • Working of Self-Organizing Maps
  • Returning to K-Means
  • K-Means Clustering
  • Perusing an Advanced SOM
  • Expanding on SOM
  • Boltzmann Machines
  • Energy-Based Models (EBM)
  • Limited Boltzmann Machine
  • Investigating Contrastive Divergence
  • Deep Belief Networks
  • Deep Boltzmann Machines
  • Building a Boltzmann Machine
  • Introducing Ubuntu on Windows
  • Introducing PyTorch
  • AutoEncoders
  • AutoEncoders: An Overview
  • AutoEncoders Intuition
  • Plan of Attack
  • Preparing an AutoEncoder
  • Overcomplete covered up layers
  • Scanty Autoencoders
  • Denoising Autoencoders
  • Contractive Autoencoders
  • Stacked Autoencoders
  • Deep Autoencoders
  • PCA, LDA, and Dimensionality Reduction
  • Dimensionality Reduction
  • Head Component Analysis (PCA)
  • PCA in Python
  • PCA in R
  • Straight Discriminant Analysis (LDA)
  • LDA in Python
  • LDA in R
  • Piece PCA
  • Piece PCA in Python
  • Piece PCA in R
  • Model Selection and Boosting
  • K-Fold Cross Validation in Python
  • Matrix Search in Python
  • K-Fold Cross Validation in R
  • Matrix Search in R
  • XGBoost
  • XGBoost in Python
  • XGBoost in R