Move from classic ML to production-ready deep learning with modern architectures and best practices.
Why This Bootcamp Matters
Deep learning has moved from research labs into every serious AI product: computer vision, speech recognition, recommendation systems, LLMs, and generative models all sit on top of deep neural networks. Reports on AI skills consistently list deep learning, advanced ML, and generative AI among the top in-demand capabilities for high-paying roles in 2025 and beyond.
Leading programs like the DeepLearning.AI Deep Learning Specialization, TensorFlow and Google courses, and fast.ai’s Practical Deep Learning for Coders all converge on the same core stack of skills:
- Understanding and building neural networks
- Training and improving CNNs for vision
- Working with sequence models (RNNs, LSTMs, Transformers)
- Applying regularization, optimization, and hyperparameter tuning in practice
Most engineers and data scientists stop at “classic ML” (scikit-learn, basic models). This bootcamp is designed as the next step: an intensive, 3-day, hands-on program that takes you into modern deep learning, with a focus on practical implementation, debugging, and model improvement — the skills that actually matter when shipping models in real projects.
Call us to learn more about this deep learning & advanced machine learning bootcamp (3 days)
Overview
Title
Deep Learning & Advanced Machine Learning Bootcamp
Duration
3 days (full-day, in-person, instructor-led)
Level
Intermediate – for participants who already understand basic ML (regression, classification, train/test split, evaluation metrics) and are comfortable with Python and pandas.
Purpose
To equip participants with practical, end-to-end deep learning skills: from tensors and neural network basics to CNNs, sequence models, regularization, and real-world training workflows.
Core Tools & Frameworks
- Python, Jupyter Notebooks
- PyTorch or TensorFlow/Keras (selectable per client / region)
- NumPy, pandas, Matplotlib/Seaborn for supporting analysis
Who This Bootcamp Is For
This program is ideal for:
- ML practitioners & data scientists who already know scikit-learn and want to move into deep learning
- Software engineers building AI-powered features and wanting to understand DL from first principles
- Applied researchers & technical leads who need to evaluate, select, and guide deep learning solutions
- Strong graduates / early-career engineers who have completed an ML fundamentals course and want to accelerate into advanced roles
Recommended prerequisites
- Solid Python skills
- Comfortable with NumPy and pandas
- Prior exposure to supervised learning (regression, classification) and evaluation metrics
What Participants Will Be Able To Do
By the end of the 3 days, participants will be able to:
- Explain how neural networks work at an intuitive and implementation level (layers, activations, loss, gradients)
- Build, train, and debug fully-connected networks for tabular data
- Design and train convolutional neural networks (CNNs) for image problems (classification, basic feature extraction)
- Understand and apply sequence models (RNNs / LSTMs) and get a high-level intuition for Transformers for text/time-series tasks
- Use modern regularization and optimization techniques: dropout, batch normalization, better initialization, learning rate schedules, and optimizers like Adam and SGD with momentum
- Apply transfer learning and fine-tuning using pre-trained models for faster, more robust results
- Perform structured error analysis and experiment design (what to change next, and why)
Design and implement a small end-to-end deep learning project (vision or sequence-based), with clear evaluation and documentation
3-Day Agenda
Day 1 – Neural Network Foundations & Training Deep Nets
Session 1: From Classic ML to Deep Learning
- Quick refresher: limitations of linear models and traditional ML on complex data
- What makes deep learning powerful: representation learning and depth
- Tensors, computation graphs, and automatic differentiation (high-level)
Hands-on focus
- Setting up the DL environment in notebooks
- First tensor operations and simple feed-forward network skeleton
Session 2: Building Your First Deep Network
- Architecture basics: layers, activations (ReLU, sigmoid, tanh), output layers for different tasks
- Loss functions: MSE, cross-entropy – when to use which
- Gradient descent and optimizers (SGD, momentum, Adam – conceptual)
- Batching, epochs, and the training loop
Hands-on focus
- Implement a small fully-connected network (e.g. for tabular or simple image-like data)
- Train–validation split, monitoring loss/accuracy curves
- First debugging steps: learning rate problems, exploding/vanishing gradients (at least at an intuitive level)
Day 1 Outcomes
Participants can:
- Build and train a simple neural network from scratch using a modern framework
- Interpret training curves and spot basic issues in optimization
- Understand how their deep network relates back to classic ML models
Day 2 – Core Architectures: CNNs, Sequence Models & Making Models Better
Session 3: Convolutional Neural Networks (CNNs) for Vision
- Why convolutions? Local receptive fields, weight sharing, and translation invariance
- Typical CNN blocks: Conv → Activation → Pooling → Fully-connected
- Common architectures in practice (e.g. ResNet-style ideas at a conceptual level)
Hands-on focus
- Build and train a small CNN for image classification (e.g. generic dataset)
- Understand overfitting visually through training/validation curves
- Try basic data augmentation and see the effect on generalization
Session 4: Improving Deep Networks in Practice
- Regularization techniques: dropout, L2 weight decay, data augmentation
- Batch normalization: stabilizing and accelerating training
- Weight initialization strategies and their impact on convergence
- Learning rate schedules: step decay, cosine, one-cycle (intuitive overview) (GitHub)
Hands-on focus
- Add dropout & batch norm to an existing model
- Run short experiments comparing “vanilla” vs “improved” model setups
Session 5: Sequence Models & Time-Dependent Data
- Types of sequence problems: text, time series, logs, clickstreams
- RNNs and LSTMs: how they process sequences and retain information
- Conceptual look at attention and Transformers: why they replaced many RNN use cases
Hands-on focus
- Train a simple sequence model (e.g. many-to-one LSTM for classification or forecasting)
- Compare baseline (e.g. classic model) vs sequence model performance and behavior
Day 2 Outcomes
Participants can:
- Implement CNNs for basic vision tasks and understand how and why they work
- Improve training stability and generalization using modern regularization & optimization
Build and train a basic sequence model and know when to reach for RNNs/LSTMs vs Transformers
Day 3 – Transfer Learning, Advanced Workflows & Capstone Project
Session 6: Transfer Learning & Pre-Trained Models
- Why transfer learning is a default strategy in modern DL
- Using pre-trained CNNs or language models as feature extractors
- Fine-tuning vs freezing layers: trade-offs and strategies
Hands-on focus
- Load a pre-trained model from a standard library (vision or text)
Adapt it to a new, smaller dataset and compare performance vs training from scratch
Session 7: Experimentation, Evaluation & Practical Patterns
- Best practices from industry and top courses:
- Clear experiment tracking and naming
- Train/validation/test splits done correctly
- Error analysis: inspecting misclassified examples; slicing by segment
- Clear experiment tracking and naming
- Practical hyperparameter tuning: what to tune first (learning rate, architecture size, regularization strength, etc.)
- Discussion of deployment and production concerns at a high level (latency, model size, monitoring), without turning this into an MLOps course
Hands-on focus
- Design 2–3 small, structured experiments on an existing model
- Use evaluation metrics and misclassification analysis to decide “what to try next”
Session 8: Capstone – End-to-End Deep Learning Project
Participants work in small teams on a mini capstone project, applying the full workflow:
- Choose a domain (e.g. images, tabular + MLP, or sequences)
- Frame the task as a supervised deep learning problem
- Prepare data (splits, basic preprocessing, augmentations if relevant)
- Select & implement an architecture (from foundations or via transfer learning)
- Train & iterate with at least one improvement cycle
- Evaluate & interpret results (metrics + error analysis)
- Present a short 5–7 minute summary:
- Problem & approach
- Model & experiments
- Results & what they’d try next with more time
- Problem & approach
Day 3 Outcomes
Participants finish with:
- A complete, end-to-end deep learning project they can refine and publish (e.g. on GitHub or in a portfolio)
- Concrete experience in making architectural and training decisions under constraints
A much stronger mental model of how advanced ML systems are actually built and improved in practice
Call us to learn more about this deep learning & advanced machine learning bootcamp (3 days)
Key Outcomes & Takeaways
After this bootcamp, participants will:
- Have a working skill set in deep learning, not just vocabulary
- Be able to design, train, and debug fully-connected networks, CNNs, and basic sequence models
- Know how to apply regularization, initialization, and tuning strategies that are taught in top global programs and used in production system
- Use transfer learning and pre-trained models to accelerate real-world projects
- Be prepared to move into specialized areas: computer vision, NLP, generative models, or MLOps
Leave with reusable code templates and a capstone project that demonstrates intermediate-level deep learning capability
FAQs
1. Is this bootcamp math-heavy?
Answer: We focus on intuition, implementation, and best practices. Some linear algebra and basic calculus concepts are discussed conceptually, but you won’t be doing long derivations.
2. Do I need to have built models before?
Answer: Yes, at a basic ML level. You should be comfortable training models with scikit-learn (or equivalent) and understanding ideas like train/test split and accuracy
3. Which framework will we use: PyTorch or TensorFlow?
Answer: We typically use [choose one: PyTorch / TensorFlow] based on the organization’s preference. The concepts you learn transfer easily between modern deep learning frameworks.
4. Is this enough to work as a Deep Learning Engineer?
Answer: This bootcamp gives you a strong intermediate foundation and a serious push forward. Becoming a full DL engineer also requires continued practice, larger projects, and often MLOps/deployment experience — this bootcamp is designed to accelerate you along that path.
5. Can this be tailored to our industry (e.g. finance, healthcare, manufacturing)?
Answer: Yes. While the core curriculum stays the same, datasets, case studies, and examples can be customized to reflect your domain and constraints.
Ready to Take the Next Step?
- Schedule a call with our trainer
Haris Aamir
Trainer
Lincoln School