Deep Learning with Python Training

RexGalaxy Academy's Deep Learning with Python Training is a practical, project-based program designed for learners who want to understand neural networks and build modern AI models using Python, TensorFlow and Keras. The course is placed inside the Python category because it focuses on Python-based deep learning workflows, practical coding, data preparation, model training and AI project development.

This training starts with Python and deep learning fundamentals, then gradually moves into neural network architecture, NumPy-based data preparation, TensorFlow workflows, Keras model building, optimization, CNNs, RNNs, transfer learning, model evaluation and deployment awareness. Learners work with real datasets and practical examples to understand how deep learning models learn patterns from images, text, sequences and structured data.

The course focuses on hands-on learning instead of only mathematical theory. Students build models step by step, train them, tune them, evaluate results, understand overfitting, improve performance and document projects professionally. The training also explains where deep learning is useful, where it is not suitable and how to think responsibly about AI model outputs.

Students should be able to:

• Use Python, NumPy, TensorFlow and Keras for practical deep learning model development.

• Understand neural networks, activation functions, loss functions, optimizers and model training workflow.

• Work with data preprocessing, feature scaling, train-test split, validation and model performance metrics.

• Create CNN-based image classification projects and understand transfer learning concepts.

• Understand RNN/LSTM sequence modelling awareness for text and time-series use cases.

• Build portfolio-ready deep learning projects with notebooks, model summaries, charts and explanations.

Start with deep learning fundamentals and understand where neural networks fit inside artificial intelligence.

• Understand the relationship between artificial intelligence, machine learning, deep learning and neural networks.

• Learn why deep learning became important for image recognition, speech, language, recommendations and automation.

• Understand the difference between traditional machine learning features and representation learning.

• Learn the basic workflow: problem definition, dataset preparation, model design, training, validation, testing and improvement.

• Understand common use cases such as image classification, text analysis, time-series forecasting and generative AI foundations.

• Learn important terms such as neurons, layers, weights, bias, epochs, batches, learning rate and model parameters.

• Understand data dependency, compute requirements, GPU awareness and why deep learning needs careful experimentation.

• Discuss limitations such as overfitting, biased data, poor labels, explainability challenges and incorrect predictions.

• Practice mapping real-world problems to deep learning tasks such as classification, regression and sequence prediction.

• Outcome: build a clear foundation for learning Python-based neural network modelling.

Prepare Python and numerical computing skills required for deep learning projects.

• Review Python fundamentals including variables, functions, loops, conditions and clean coding habits.

• Work with lists, dictionaries, arrays and reusable helper functions for data handling.

• Use NumPy arrays for numerical operations, reshaping, slicing, filtering and vectorized calculations.

• Understand matrix shapes, dimensions, broadcasting awareness and why tensor shapes matter in deep learning.

• Learn data loading basics using CSV, image folders or common dataset utilities.

• Practice splitting datasets into training, validation and testing sets.

• Learn normalization, scaling, encoding labels and converting data into model-ready formats.

• Understand missing values, wrong formats, class imbalance awareness and dataset quality checks.

• Use notebooks for documenting experiments, outputs, charts and observations.

• Outcome: prepare clean and correctly shaped data for deep learning model training.

Review machine learning concepts that are essential before moving deeply into neural networks.

• Understand supervised learning, unsupervised learning, classification, regression, features and labels.

• Learn train-test split, validation data and why evaluation on unseen data matters.

• Understand common metrics such as accuracy, precision, recall, F1-score, confusion matrix, MAE and RMSE.

• Learn overfitting, underfitting, bias-variance awareness and generalization concepts.

• Understand feature scaling, encoding, preprocessing pipelines and data leakage risks.

• Compare traditional machine learning models with deep learning models at a beginner level.

• Learn when deep learning may be useful and when simpler machine learning models may be better.

• Practice evaluating simple models and interpreting performance results.

• Understand experiment tracking basics such as changing one parameter at a time and comparing results.

• Outcome: connect machine learning fundamentals with deep learning model development.

Understand how artificial neurons and neural networks process information.

• Learn the concept of a perceptron, inputs, weights, bias, weighted sum and output.

• Understand layers including input layer, hidden layers and output layer.

• Learn forward propagation and how data flows through a neural network to generate predictions.

• Understand parameters, model architecture, dense layers and the meaning of network depth and width.

• Learn how classification and regression output layers differ.

• Understand why neural networks need nonlinear activation functions to learn complex patterns.

• Practice drawing neural network diagrams and explaining each layer's role.

• Build intuition for how weights are updated during training without getting lost in heavy mathematics.

• Understand model summary outputs and how to read layer shapes and parameter counts.

• Outcome: explain neural network structure and forward pass clearly.

Learn the functions and optimization methods that help neural networks learn from data.

• Understand activation functions such as ReLU, sigmoid, tanh and softmax with practical use cases.

• Learn loss functions for regression, binary classification and multi-class classification.

• Understand optimizers such as SGD, Adam and learning rate awareness.

• Learn backpropagation conceptually and how errors are used to update weights.

• Understand epochs, batch size, iterations and how training progresses over time.

• Learn training curves, validation curves and how loss or accuracy changes during learning.

• Understand gradient descent intuition and why poor learning rates can harm training.

• Practice choosing output activation and loss function based on problem type.

• Learn common training problems such as slow convergence, unstable loss and vanishing gradients awareness.

• Outcome: understand how models learn and how training settings affect performance.

Use TensorFlow and Keras to build deep learning models with practical Python workflows.

• Set up TensorFlow, Keras and notebook-based development environment awareness.

• Understand tensors, TensorFlow operations and the role of Keras as a high-level modelling API.

• Build Sequential models using Dense layers, activation functions and output layers.

• Compile models with optimizer, loss function and metrics.

• Train models using fit, validate performance and inspect training history.

• Use model.summary to understand architecture and parameter counts.

• Learn how to prepare input shapes and output dimensions correctly.

• Practice classification and regression models using structured datasets.

• Understand common TensorFlow errors related to shapes, labels, data types and incompatible loss functions.

• Outcome: create and train beginner neural network models using Python, TensorFlow and Keras.

Build complete neural network workflows from dataset preparation to model evaluation.

• Load datasets, inspect columns, clean data and prepare features and labels.

• Split data into training, validation and testing sets with correct workflow discipline.

• Scale numerical features and encode categorical labels where required.

• Build neural network models for binary classification, multi-class classification and regression.

• Train models while tracking loss, accuracy and validation performance.

• Evaluate models using metrics and interpret results in practical language.

• Create confusion matrices, classification reports and prediction comparisons.

• Visualize training history using loss and accuracy curves.

• Document model architecture, training settings, results and improvement ideas.

• Outcome: complete end-to-end TensorFlow model training and evaluation workflows.

Improve deep learning models by controlling overfitting and tuning important parameters.

• Understand overfitting signs such as high training accuracy and poor validation performance.

• Learn regularization techniques including dropout, L2 awareness and simpler model design.

• Use early stopping awareness to stop training when validation performance stops improving.

• Tune hyperparameters such as learning rate, batch size, number of layers, neurons and epochs.

• Understand data augmentation awareness for improving image model generalization.

• Learn class imbalance handling awareness and why accuracy alone can be misleading.

• Compare multiple experiments and choose models based on validation and test performance.

• Understand model checkpoints and saving best-performing model versions.

• Practice improving a weak model step by step with documented changes.

• Outcome: improve model reliability and avoid common deep learning training mistakes.

Build computer vision models using Convolutional Neural Networks.

• Understand image data representation, pixels, channels, height, width and tensor shapes.

• Learn CNN concepts including convolution, filters, feature maps, pooling and flattening.

• Build CNN architectures using Conv2D, MaxPooling, Flatten and Dense layers.

• Train image classification models on simple datasets with multiple classes.

• Use image preprocessing, resizing, normalization and batching workflows.

• Understand data augmentation for rotation, flipping, zooming and brightness changes awareness.

• Evaluate CNN models using accuracy, confusion matrix and sample predictions.

• Learn how CNNs detect edges, textures, shapes and higher-level image patterns conceptually.

• Practice projects such as object category classification or handwritten digit recognition.

• Outcome: build and explain CNN-based image classification models.

Understand sequence modelling concepts using RNNs, LSTMs and time-based data awareness.

• Learn why sequence data requires models that consider order and context.

• Understand examples such as text, speech, sensor readings, stock prices and time-series data.

• Learn basic RNN concepts and why standard neural networks are limited for sequential tasks.

• Understand LSTM and GRU awareness for handling longer sequence dependencies.

• Learn text preprocessing awareness including tokenization, sequences, padding and embeddings.

• Understand sequence classification and next-value prediction at a beginner level.

• Practice simple sequence modelling examples using prepared datasets.

• Learn limitations of RNNs and how modern transformer models extend sequence modelling.

• Discuss NLP and time-series project possibilities using deep learning.

• Outcome: understand recurrent model concepts and sequence modelling foundations.

Use transfer learning and learn how trained models can be reused for faster project development.

• Understand transfer learning and why pretrained models help when data or compute is limited.

• Learn pretrained model awareness such as MobileNet, VGG, ResNet or similar CNN architectures.

• Use feature extraction and fine-tuning concepts for image classification tasks.

• Understand when to freeze layers and when to train selected layers.

• Compare training from scratch versus using pretrained models.

• Save and load TensorFlow/Keras models for reuse and project submission.

• Understand model formats, checkpoints and basic deployment-ready packaging awareness.

• Learn deployment basics such as serving predictions through a simple API or web app awareness.

• Discuss model monitoring, input validation and responsible use after deployment.

• Outcome: use pretrained models and prepare trained models for practical projects.

Complete deep learning projects and prepare for interviews, portfolios and AI learning growth.

• Build a structured-data neural network project with preprocessing, model training, evaluation and explanation.

• Build an image classification project using CNN or transfer learning with real image datasets.

• Build a sequence or text classification awareness project using prepared sequence data.

• Document projects with problem statement, dataset source, preprocessing steps, architecture, metrics and results.

• Create visual outputs such as training curves, confusion matrices, prediction samples and error analysis.

• Prepare GitHub repositories with notebooks, requirements, README files, screenshots and project summaries.

• Practice interview questions on neural networks, activation functions, loss functions, optimizers, CNNs, RNNs and TensorFlow.

• Learn how to explain model limitations, improvements and ethical considerations.

• Prepare resume bullet points for Python, deep learning, TensorFlow, Keras, CNN and AI projects.

• Outcome: build a practical deep learning portfolio and present projects confidently.