Python  AI/ML

Fee :  ₹29,500 + GST  ₹5310, Duration : 5 months,  Contact : patkar@rajeshpatkar.com

1. Deep Learning Foundations

   • What is Deep Learning?

   • Differences between Machine Learning and Deep Learning

   • Applications of Deep Learning (vision, NLP, reinforcement learning, etc.)

2. Mathematics for Deep Learning

   • Linear Algebra: Vectors, matrices, and operations

   • Calculus: Derivatives, gradients, and optimization basics

   • Probability: Basic probability and distributions

3. Neural Networks Overview

   • Biological inspiration and artificial neurons

   • Basic architecture: Input, hidden, and output layers

   • Activation functions (ReLU, Sigmoid, Tanh, Softmax)

4. Introduction to TensorFlow and PyTorch

   • Installing and setting up frameworks

   • Basic TensorFlow operations

   • Introduction to PyTorch tensors

1. Building Blocks

   • Forward propagation

   • Backpropagation and gradient descent

   • Loss functions (MSE, Cross-Entropy)

   • Optimizers (SGD, Adam, RMSprop)

2. Hands-On Neural Networks

   • Building a neural network from scratch using NumPy

   • Building the same neural network using TensorFlow/Keras and PyTorch

3. Training and Evaluation

   • Train, validation, and test splits

   • Overfitting and underfitting

   • Regularization techniques: L1/L2 regularization, dropout, and data augmentation

1. Understanding Convolutions

   • Convolution operations and kernels

   • Feature maps, pooling layers, and padding

2. CNN Architectures

   • Building basic CNNs

   • Key architectures: LeNet, AlexNet, VGG, ResNet

3. Advanced Topics in CNNs

   • Transfer learning: Using pre-trained models

   • Fine

1. Sequential Modeling Basics

   • RNNs: Architecture and working principle

   • Challenges: Vanishing gradients and long-term dependencies

2. Advanced RNNs

   • Long Short-Term Memory (LSTM) networks

   • Gated Recurrent Units (GRUs)

3. Applications of RNNs

   • Time series forecasting

   • Text generation

1. Attention Mechanisms

   1. Understanding attention in sequential models

   2. Self-attention and multi-head attention

2. Transformers Architecture

   1. Introduction to the Transformer model

   2. Positional encodings and scaling

3. Pretrained Models and Applications

   1. BERT for text classification

   2. GPT for text generation

4. Hands-On Projects:

   1. Sentiment analysis using BERT

   2. Fine-tuning GPT for custom text generation

1. Autoencoders

   • Encoder-decoder architecture

   • Applications: Dimensionality reduction and anomaly detection

2. Variational Autoencoders (VAEs)

   • Understanding probabilistic models

   • Applications: Generative modeling

3. Generative Adversarial Networks (GANs)

   • GAN architecture: Generator and discriminator

   • Applications: Image generation, style transfer

4. Diffusion Models

   • Basics of Diffusion Models

   • Understanding the forward and reverse diffusion processes

   • Noise schedules and Markov chains

   • Training Diffusion Models

5. Applications of Diffusion Models

   • Image synthesis 

1. Model Optimization

   • Quantization and pruning for lightweight models

   • Knowledge distillation

2. Deployment Pipelines

   • Model serving using TensorFlow Serving or ONNX Runtime

   • Deploying models as REST APIs with Flask/FastAPI

3. Real-Time Model Serving

   • Using open-source tools like TensorFlow Serving for scalable deployment

1. Using APIs from popular LLM providers:

2. Fundamentals of prompt engineering:

   • Structuring effective prompts for specific tasks.

   • Fine-tuning prompts for domain-specific applications.

3. Fine-tuning LLMs for advanced use cases:

   • Overview of custom training using open-source tools.

4. AI as a Service (AIaaS)

5. Building AI-Powered Web Apps

• Integrating AI models into Flask or FastAPI backends.

• Consuming AI services with:

  • RESTful APIs

  • gRPC for efficient communication.

4. Real-Time AI Systems

• Using WebSockets for real-time AI integrations.