Deep Learning Certification Training
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Learners : 1080
About Course
Our training in deep learning is The first thing this deep learning introduction will do is provide an overview of deep learning, its applications, and platforms. You can learn more about deep learning by participating in online training. A branch of machine learning called deep learning studies algorithms that are modeled after the structure and operation of the human brain. Artificial intelligence (AI) includes machine learning, which includes deep learning.
Deep Learning Training Course Syllabus
✔ Python for AI & ML
✔ Business Statistics
✔ Data Visualization (EDA)
✔ Regression & Classification
✔ Training, Validation & Testing
✔ Measures of Model Performance
✔ Linear Regression
✔ Logistic Regression
✔ K-NN Classification
✔ Naïve Bayes Classifiers
✔ SVM
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✔ Decision Trees
✔ Bagging
✔ Random Forest
✔ Feature Engineering
✔ Model Development & Improvement
✔ Model Validation & Diagnostics
✔ Grid Search
✔ Cross Validation
✔ Content-based Recommender Systems
✔ Collaborative Filtering (User & Item based)
✔ Time Series Forecasting
✔ Content-based Recommender Systems
✔ Text Mining
✔ CNN (Computer Vision)
✔ RNN/LSTM
✔ TensorFlow
✔ Keras
✔ ANN
✔ RNN
✔ Big Data Analytics
| Live Instructor Based Training With Software |
| Lifetime access and 24×7 support |
| Certification Oriented content |
| Hands-On complete Real-time training |
| Get a certificate on course completion |
| Flexible Schedules |
| Live Recorded Videos Access |
| Study Material Provided |
Deep Learning Training - Upcoming Batches
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Live Virtual Training
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Schedule your sessions at your comfortable timings.
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Instructor-led training, Real-time projects
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Certification Guidance.
Self-Paced Learning
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Complete set of live-online training sessions recorded videos.
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Learn technology at your own pace.
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Get access for lifetime.
Corporate Training
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Learn As A Full Day Schedule With Discussions, Exercises,
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Practical Use Cases
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Design Your Own Syllabus Based
Deep Learning Training FAQ'S
Artificial neural networks, which are used in deep learning algorithms, are modeled after the structure and operation of the brain. While developing a deep learning network, Alexey Grigorevich Ivakhnenko published the first general in the middle of the 1960s. A variety of domains, including computer vision, speech recognition, natural language processing, etc., are well suited for deep learning.
By learning through VISWA Online Trainings, advance in your job.
- Supervised learning is a system in which both input and desired output data are provided. Input and output data are labeled to provide a learning basis for future data processing.
- Unsupervised procedure does not need labeling information explicitly, and the operations can be carried out without the same. The common unsupervised learning method is cluster analysis. It is used for exploratory data analysis to find hidden patterns or grouping in data.
- Computer vision
- Natural language processing and pattern recognition
- Image recognition and processing
- Machine translation
- Sentiment analysis
- Question Answering system
- Object Classification and Detection
- Automatic Handwriting Generation
- Automatic Text Generation.
Both shallow and deep networks can approximate any function and are equally effective. Deeper networks, however, can be substantially more productive in terms of computation and number of parameters for the same degree of accuracy. Deep representations can be produced by deeper networks. The network picks up a new, more abstract representation of the input at each tier.
The most frequent problem in deep learning is overfitting. Typically, it happens when a deep learning algorithm recognises the sound of particular data. It also manifests when a specific algorithm is well suited to the data and manifests when a model or algorithm exhibits high variance and low bias.
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