Deep Learning using TensorFlow & PyTorch: Master Advanced AI
Master the cutting-edge field of Deep Learning with BinnBash Academy's comprehensive course using TensorFlow and PyTorch. Learn neural network architectures, CNNs, RNNs, Transformers, GANs, NLP, computer vision, and model deployment. Build a powerful portfolio with intensive real-time live projects to become a cutting-edge AI Engineer or Researcher!
Build Intelligent Systems!Who Should Enroll in this Deep Learning Course?
This course is ideal for individuals eager to dive deep into neural networks and advanced AI applications:
- Aspiring Deep Learning Engineers & AI Researchers.
- Machine Learning Engineers looking to specialize in Deep Learning.
- Data Scientists seeking to implement complex neural network models.
- Software Developers with Python experience interested in AI/DL.
- Anyone with a strong mathematical and programming background eager to build advanced AI solutions.
Deep Learning Course Prerequisites
- Solid understanding of Python programming (intermediate level).
- Foundational knowledge of Machine Learning concepts (supervised/unsupervised learning).
- Basic understanding of linear algebra, calculus, and statistics.
- Experience with NumPy and Pandas is highly recommended.
- A strong analytical and problem-solving mindset.
Key Deep Learning Tools & Concepts Covered
Hands-on mastery of leading Deep Learning frameworks, advanced architectures, and deployment strategies for cutting-edge AI solutions.
Deep Learning: Comprehensive Syllabus & Intensive Real-Time Projects
Module 1: Deep Learning Fundamentals & Frameworks
- Introduction to Deep Learning: History, Why Deep?
- Neural Network Basics: Perceptrons, Activation Functions.
- Multi-Layer Perceptrons (MLPs) & Backpropagation.
- Introduction to TensorFlow/Keras & PyTorch: Core Concepts.
- Setting up GPU Environment (Google Colab, cloud concepts).
- Live Project: Build and train a basic feedforward neural network in both TensorFlow/Keras and PyTorch for a simple classification task (e.g., MNIST).
Tools & Concepts:
- TensorFlow, Keras, PyTorch, NumPy, Pandas.
Expected Outcomes:
- Understand core DL concepts.
- Proficiency in TensorFlow/Keras and PyTorch basics.
- Set up and utilize GPU environments.
Module 2: Convolutional Neural Networks (CNNs) for Computer Vision
- Introduction to Computer Vision & Image Data.
- Convolutional Layers, Pooling Layers, Activation.
- Building and Training CNN Architectures (LeNet, AlexNet concepts).
- Image Augmentation & Transfer Learning with Pre-trained CNNs (VGG, ResNet).
- Object Detection & Image Segmentation (Foundational Concepts).
- Live Project: Develop a CNN model for image classification (e.g., CIFAR-10, custom dataset) and apply transfer learning to improve performance.
Tools & Concepts:
- TensorFlow/Keras, PyTorch, OpenCV (basics), ImageNet.
Expected Outcomes:
- Design and implement CNNs.
- Apply transfer learning for computer vision.
- Understand object detection/segmentation basics.
Module 3: Recurrent Neural Networks (RNNs) & Sequence Models for NLP
- Introduction to Natural Language Processing (NLP) & Text Data.
- Recurrent Neural Networks (RNNs) for Sequence Modeling.
- Long Short-Term Memory (LSTM) & Gated Recurrent Units (GRU).
- Word Embeddings (Word2Vec, GloVe, FastText).
- Sequence-to-Sequence Models & Attention Mechanism (concepts).
- Live Project: Build an LSTM model for sentiment analysis or text classification on a real-world text dataset.
Tools & Concepts:
- TensorFlow/Keras, PyTorch, NLTK, SpaCy, Gensim.
Expected Outcomes:
- Implement RNNs for sequence data.
- Utilize LSTMs and GRUs effectively.
- Apply word embeddings in NLP tasks.
Module 4: Advanced Deep Learning Architectures & Generative Models
- Transformers: Attention Is All You Need, Encoder-Decoder Architecture.
- Introduction to Large Language Models (LLMs) & Fine-tuning (concepts).
- Generative Adversarial Networks (GANs): Theory & Implementation (DCGAN, Conditional GAN concepts).
- Variational Autoencoders (VAEs) - concepts.
- Diffusion Models (concepts).
- Live Project: Implement a basic Transformer encoder for a sequence task or generate new data samples using a simple GAN architecture.
Tools & Concepts:
- Hugging Face Transformers (basics), TensorFlow/PyTorch for GANs.
Expected Outcomes:
- Understand Transformer architecture.
- Grasp concepts of generative models (GANs, VAEs).
- Explore cutting-edge DL applications.
Module 5: Deep Learning for Specific Applications & Optimization
- Advanced Computer Vision: Semantic Segmentation, Instance Segmentation (concepts).
- Advanced NLP: Question Answering, Text Summarization, Machine Translation (concepts).
- Reinforcement Learning Basics (concepts).
- Model Optimization: Regularization, Dropout, Batch Normalization.
- Hyperparameter Optimization Techniques (Grid Search, Random Search, Bayesian Optimization concepts).
- Debugging & Interpreting Deep Learning Models.
- Live Project: Optimize a pre-built CNN or RNN model using various regularization and optimization techniques, and analyze its performance improvements.
Tools & Concepts:
- TensorFlow/PyTorch, Scikit-learn (for hyperparameter tuning).
Expected Outcomes:
- Apply DL to specialized domains.
- Optimize deep learning models effectively.
- Interpret and debug complex models.
Module 6: Deep Learning Deployment & Intensive Capstone Projects
- Model Deployment Strategies: Flask/Streamlit for Web Apps, FastAPI, TensorFlow Serving/TorchServe.
- Containerization with Docker for DL Models.
- Introduction to MLOps for Deep Learning: Versioning, Monitoring, CI/CD (concepts).
- Cloud AI Services for Deployment (AWS SageMaker, Google AI Platform, Azure ML - concepts).
- Ethical AI in Deep Learning: Bias, Explainability (XAI), Privacy.
- Intensive Real-time Capstone Project: Develop and deploy an end-to-end Deep Learning solution for a real client or a complex simulated problem. This includes data collection, advanced preprocessing, model selection (CNN/RNN/Transformer), training, rigorous evaluation, and deployment as a functional, accessible application.
- Building a Professional Deep Learning Portfolio: Showcasing deployed models, research papers (if applicable), and problem-solving methodologies.
- Career Guidance: Deep Learning Engineer, AI Research Scientist, Computer Vision Engineer, NLP Engineer, MLOps Engineer, Freelancing, Mock Interviews.
Tools & Concepts:
- Flask/Streamlit/FastAPI, Docker (concepts), Cloud platforms (concepts).
- Intensive Live Project Work, Client Communication, Portfolio Building, Career Prep.
Expected Outcomes:
- Deploy deep learning models into production.
- Understand MLOps principles for DL.
- Gain extensive practical experience with real-world Deep Learning project lifecycle, leading to tangible, deployable AI solutions.
- Prepare for a high-level Deep Learning/AI career.
This course provides hands-on, in-depth expertise to make you a proficient and job-ready Deep Learning professional, with a strong emphasis on advanced model building, real-time project implementation, and building a powerful, results-driven portfolio!
Deep Learning Professional Roles and Responsibilities in Real-Time Scenarios & Live Projects
Gain hands-on experience by working on live projects, understanding the real-time responsibilities of a Deep Learning professional in leading tech companies, AI research labs, and innovative startups. Our curriculum aligns with industry demands for cutting-edge AI practitioners.
Deep Learning Engineer
Designs, develops, and deploys advanced neural network models for various AI applications, as done at Google DeepMind.
AI Research Scientist
Conducts research into new AI algorithms and deep learning architectures, pushing the boundaries of AI capabilities, similar to work at OpenAI.
Computer Vision Engineer (DL)
Specializes in building deep learning models for image and video analysis, including advanced object detection and segmentation, common at NVIDIA.
Natural Language Processing (NLP) Engineer (DL)
Develops deep learning models to understand, process, and generate human language, for applications like LLMs and advanced chatbots, like at Hugging Face.
MLOps Engineer (Deep Learning)
Focuses on streamlining the deployment, monitoring, and maintenance of complex deep learning models in production environments.
AI Infrastructure Engineer
Builds and maintains the underlying hardware and software infrastructure required for training and deploying large-scale deep learning models.
Generative AI Specialist
Focuses on developing and applying generative models (GANs, Diffusion Models) for content creation and data synthesis.
Deep Learning Consultant
Advises businesses on integrating deep learning solutions and optimizing their AI strategies.
Our Alumni Works Here!
Rohan Gupta
Deep Learning Engineer
Ananya Sharma
AI Research Scientist
Vikram Singh
Computer Vision Engineer
Priya Patel
NLP Engineer
Karan Desai
MLOps Engineer
Meera Rao
AI Infrastructure Engineer
Arjun Kumar
Generative AI Specialist
Sneha Reddy
Deep Learning Consultant
Devansh Mehta
Deep Learning Trainee
Aditi Singh
Senior DL Scientist
Rohan Gupta
Deep Learning Engineer
Ananya Sharma
AI Research Scientist
Vikram Singh
Computer Vision Engineer
Priya Patel
NLP Engineer
Karan Desai
MLOps Engineer
Meera Rao
AI Infrastructure Engineer
Arjun Kumar
Generative AI Specialist
Sneha Reddy
Deep Learning Consultant
Devansh Mehta
Deep Learning Trainee
Aditi Singh
Senior DL Scientist
What Our Deep Learning Students Say
"This Deep Learning course is phenomenal! I now have a deep understanding of neural networks and can build complex models with TensorFlow and PyTorch."
"The hands-on projects for CNNs and RNNs were incredibly challenging and rewarding. I feel confident tackling real-world computer vision and NLP problems."
"As an ML Engineer, this course was exactly what I needed to specialize in Deep Learning. The modules on Transformers and GANs were cutting-edge."
"BinnBash Academy's focus on real-time model deployment and MLOps truly sets it apart. I gained practical experience that made me ready for production environments."
"The instructors are highly knowledgeable and provide in-depth insights into advanced architectures and ethical AI considerations in Deep Learning."
"I highly recommend this course for anyone looking to build a career in advanced AI. It's comprehensive, challenging, and truly prepares you for the future."
"From mastering TensorFlow and PyTorch to understanding generative models, every aspect was covered in detail. I feel fully prepared for a top-tier AI role."
"The emphasis on building a professional portfolio with deployed models and career guidance was extremely helpful. BinnBash truly supports your job search in AI."
"Learning about model optimization and hyperparameter tuning gave me the tools to build truly high-performing deep learning systems."
"The practical approach to learning, combined with advanced theory and intensive real-time projects, made this course stand out from others."
Deep Learning Job Roles After This Course
Deep Learning Engineer
AI Research Scientist
Computer Vision Engineer (DL)
Natural Language Processing (NLP) Engineer (DL)
MLOps Engineer (Deep Learning)
AI Infrastructure Engineer
Generative AI Specialist
Deep Learning Consultant