Deep Learning Training by Experts

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Our Training Process

Deep Learning - Syllabus, Fees & Duration

MODULE 1

  • Introduction to Tensor Flow
  • Computational Graph
  • Key highlights
  • Creating a Graph
  • Regression example
  • Gradient Descent
  • TensorBoard
  • Modularity
  • Sharing Variables
  • Keras Perceptrons
  • What is a Perceptron?
  • XOR Gate

MODULE 2

  • Activation Functions
  • Sigmoid
  • ReLU
  • Hyperbolic Fns, Softmax Artificial Neural Networks
  • Introduction
  • Perceptron Training Rule
  • Gradient Descent Rule

MODULE 3

  • Gradient Descent and Backpropagation
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • Some problems in ANN Optimization and Regularization
  • Overfitting and Capacity
  • Cross-Validation
  • Feature Selection
  • Regularization
  • Hyperparameters

MODULE 4

  • Introduction to Convolutional Neural Networks
  • Introduction to CNNs
  • Kernel filter
  • Principles behind CNNs
  • Multiple Filters
  • CNN applications Introduction to Recurrent Neural Networks
  • Introduction to RNNs
  • Unfolded RNNs
  • Seq2Seq RNNs
  • LSTM
  • RNN applications

MODULE 5

Course Fees
10000+
20+
50+
25+

Deep Learning Jobs in Toowoomba

Enjoy the demand

Find jobs related to Deep Learning in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Toowoomba, chennai and europe countries. You can find many jobs for freshers related to the job positions in Toowoomba.

  • Software Engineer
  • Research Analyst
  • Data Analyst
  • Data Scientist
  • Data Engine
  • Image Recognition
  • Software Developer
  • Research Scientist
  • Instructor for Deep Learning
  • Applied Scientist

Deep Learning Internship/Course Details

Deep Learning internship jobs in Toowoomba
Deep Learning Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets. Companies like to hire people who have completed this deep learning course. Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network. Deep learning models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications. . This deep learning course in Toowoomba is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation. Students receive practical experience by working on real-world projects. Deep learning is important because it automates feature generation, works well with unstructured data, has improved self-learning capabilities, supports parallel and distributed algorithms, is cost-effective, has advanced analytics, and is scalable. Python is the language of deep learning.

List of All Courses & Internship by TechnoMaster

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The enviable salary packages and track record of our previous students are the proof of our excellence. Please go through our students' reviews about our training methods and faculty and compare it to the recorded video classes that most of the other institutes offer. See for yourself how TechnoMaster is truly unique.

List of Training Institutes / Companies in Toowoomba

  • UniversityOfSouthernQueensland(UniSQ) | Location details: UniSQ Toowoomba, 487-535 West St, Darling Heights QLD 4350, Australia | Classification: University, University | Visit Online: unisq.edu.au | Contact Number (Helpline): +61 1800 269 500
 courses in Toowoomba
Other research shows. Institutions and perceptions are an important element of transformation (Mwangi, 2006), so it is appropriate that the dynamics of tourism transformation have been frequently investigated using resident perceptions of the industry (Allen, Long, Perdue and Kieselback, 1988; Andereck, Valentine, Knopf and Vogt, 2005; Andriotis, 2005; Ap, 1992; Belisle and Hoy, 1980; Besculides, Lee and McCormick, 2002; Harrill, 200 ; Horn and Simmons, 2002; Johnson, et al. , 199 ; Perdue, Long and Allen, 1990). Arguably, tourism can deliver socio-cultural transformations (Ratz, 2000; Sebastian and Rajagopalan, 2009). The aim was to obtain a measurement system for social norms and community perceptions to inform a broader, more detailed study into the tourism transformation process. Diedrich and Garcia-Buades (2009) show that as tourism grows and has more severe impacts on an area, so does the population's perception of tourism implications. These studies have often been undertaken for two primary reasons: to overcome barriers to successful and sustainable tourism development (commonly termed paradoxes) and to provide insight into the level of impact tourism has on the community (Diedrich and Garcia- Baudes, 2009). It is often postulated that local or regional governments should self-direct and play a greater role in tourism development because structural changes and impacts have the greatest effect and can be more readily observed at the local level (Adams, Dixon and Rimmer, 2001; Milne and Ateljevic, 2001; Pavlovich, 2003; Haung, 200 ) and, at this level, institutional modifications and planned intervention are more likely to be effective (Roberts, 200 ; McLennan, 2005; Sebastian and Rajagoplan, 2009). Paradoxes often occur if tourism is adopted simply for the economic benefits it can provide, such as employment opportunities, increased income and standards of living and improvements in infrastructure (Archer and Cooper, 1998; Lindberg, 2001; Liu and Var, 1986; Allen, Hafer, Long and Perdue, 1993) as it can also have negative impacts, such as inflation, leakage of tourism revenue, changes in value systems and behaviour, crowding, littering and water shortages (Buckley, 2001; Ceballos-Lascurain, 1996; Mathieson and Wall, 1982). For example, Saarinen (200 ) argued that a destination’s image, knowledge, meanings and natural and cultural features over slowly stereotype and modify over the course of the transformation process, resulting in a loss of differentiation between destinations.

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