Artificial Intelligence Course in Kolkata
Master deep neural networks, generative AI, and computer vision with the Artificial Intelligence Course in Kolkata at Acesoftech Academy. Build state-of-the-art models with Python, PyTorch, Transformers, LLMs, and OpenCV with 100% placement support.
Accelerate Your Technical Career in Kolkata
Artificial intelligence is transforming every global industry. For foundational data and predictive modeling pathways, check our Machine Learning Course in Kolkata and Data Science Course in Kolkata, or master the underlying language in our Python Course in Kolkata.
Comprehensive 12-Module Curriculum
Python & GPU Computing for AI
NumPy tensor mathematics, CUDA acceleration, torch.cuda profiling, and asynchronous pipelines.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Neural Networks & Deep Learning with PyTorch
Multi-layer perceptron, backpropagation calculus, activation functions, and autograd.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Optimization Algorithms & Loss Dynamics
SGD, Adam, AdamW, learning rate scheduling (Cosine Annealing), and mixed precision (FP16).
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Computer Vision & Convolutional Neural Networks
Convolution operations, ResNet architectures, data augmentations, and transfer learning.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Object Detection & Segmentation
YOLOv8/YOLOv11 architectures, bounding boxes, non-max suppression, and semantic segmentation with U-Net.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Recurrent Neural Networks & LSTMs
Sequential data processing, vanishing gradients, LSTM cells, and GRU architectures.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
The Transformer Architecture & Self-Attention
Scaled dot-product attention, multi-head attention, positional encoding, and encoder-decoder stacks.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Natural Language Processing with Hugging Face
BERT, RoBERTa, tokenizers, sequence classification, and named entity recognition (NER).
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Large Language Models (LLMs) & Fine-Tuning
Decoder-only LLMs, causal language modeling, parameter-efficient fine-tuning (LoRA/QLoRA).
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Retrieval-Augmented Generation (RAG)
Vector databases (Chroma, Pinecone), document chunking, semantic search, and LangChain/LlamaIndex.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Generative AI: Diffusion Models & VAEs
Variational autoencoders, latent diffusion models, Stable Diffusion mechanics, and noise scheduling.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
AI Model Deployment, MLOps & Placement
Serving models with vLLM, Docker containerization, GPU cloud deployment, and MNC placement support.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
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Frequently Asked Questions
The course covers Deep Learning, PyTorch, CNNs, Transformers, LLMs, RAG, Diffusion Models, and GPU deployment.
No, we start with core Python programming and linear algebra before moving to deep neural networks.
Yes, parameter-efficient fine-tuning (PEFT/LoRA) of open-source LLMs is thoroughly covered.
Yes, 100% placement support is provided with technical interview drills and recruiter referrals.
We provide GPU cloud notebook environments (Google Colab Pro / Kaggle GPU) during training.
Weekday batches and dedicated weekend sessions are available with personal mentorship.
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