Generative AI Certification Online Training
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Learners : 1080
About Course
Welcome to the Generative AI Course, where we will explore the cutting-edge innovations that will influence artificial intelligence in the future. We cover a wide range of topics in this extensive program, from fundamental ideas to sophisticated methods, all aimed at giving you the tools you need to successfully navigate the quickly changing field of artificial intelligence. Enroll now to earn your certification.
Generative AI Training Course Syllabus
- Overview of Generative AI
- Generative AI vs. Traditional AI
- Use Cases
- Understanding AI: Basics and Use Cases
- Differentiating ML, DL and AI
- What is NLP?
- History of NLP
- NLP End to end workflow
- Stopwords
- Tokenization
- Stemming
- Lemmatization
- POS tagging
- TFIDF
- One hot encoding
- Bag of words
- Unigram
- Bigram
- ngram
- Word embeddings Skip Gram
- Word2vec model
- RNN
- LSTM Models & GRU Models
- Transfer learning
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
- LLM
- Use Cases
- Text Generation
- Chatbot Creation
- Foundations of Generative Models & LLM
- Generative Adversarial Networks (GANs)
- Autoencoders in Generative AI
- Significance of Transformers in AI
- “Attention is All You Need” – Transformer Architecture
- Reinforcement Learning
- Encoder Models i.e.
- BERT
- Decoder Models GPT
- Encoder Decoder Model i.e.
- T5
- Instruction fine-tuning
- Fine-tuning on a single task
- Multi-task instruction fine-tuning
- Model evaluation
- Benchmarks
- Parameter efficient fine-tuning (PEFT)
- PEFT techniques 1: LoRA
- PEFT techniques 2: Soft prompts
- Lab 2 walkthrough
- Rouge1
- BLEU
- Meteor
- CIDEr
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
- In-depth GCP
- Model Evaluation
- Prompt Design
- Azure ML
- Azure Cognitive Services
- Azure Databricks
- AWS Sagemaker
- AWS Jumpstart
- AWS Bedrock
- Responsible AI
- Google’s Approach
- Ethical Issues
| 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 |
Generative AI 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
Generative AI Training FAQ'S
While generative models comprehend the underlying data distribution to generate new samples, discriminative models learn the decision boundary between classes.
For tasks like image production, text generation, and anomaly detection, I’ve worked a lot with autoregressive models like PixelCNN and PixelRNN, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
Measures like the Inception Score (IS), the Frechet Inception Distance (FID), or human assessments are useful for assessing the accuracy, variety, and quality of samples that are generated.
I oversaw a project that produced finely detailed, high-resolution landscape photos. It was difficult because of how intricate the natural landscape was. To get realistic results, I used a progressive GAN architecture together with transfer learning strategies.
Mode collapse in GANs is mitigated by various techniques such as spectrum normalization, mini-batch discriminating, and combining different loss functions as WGAN-GP.
By learning Generative AI through VISWA Online Trainings, advance in your job.
