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Deep Learning Bootcamp: Neural Networks with Python, PyTorch
Rating: 4.4 out of 5(20 ratings)
201 students

Deep Learning Bootcamp: Neural Networks with Python, PyTorch

Master Neural Networks, DNNs, and CNNs with Python, PyTorch, and TensorFlow in this all-in-one Deep Learning Bootcamp.
Created byAI Sciences
Last updated 12/2025
English

What you'll learn

  • • The basics of Machine Learning.
  • • The basics of Neural Networks.
  • • The basics of training a Deep Neural Network (DNN) using Gradient Descent Algorithm.
  • • Using Deep Learning for IRIS dataset.
  • • A solid understanding of tensors and their operations in PyTorch.
  • • The ability to build and train basic to complex neural networks.
  • • Knowledge of different loss functions, optimizers, and activation functions.
  • • A completed project on brain tumor detection from MRI images, showcasing your skills in deep learning and PyTorch.
  • • A Solid Grasp of TensorFlow Basics
  • • Hands-on Experience in Building Deep Learning Models
  • • Knowledge of Model Training, Evaluation, and Optimization
  • • Confidence to Explore More Complex AI and Machine Learning Projects

Course content

3 sections • 153 lectures • 14h 21m total length

  • Promo & Highlights12:19
  • Introduction: Introduction to Instructor and Aisciences2:25
  • Links for the Course's Materials and Codes0:10
  • Basics of Deep Learning: Problem to Solve Part 12:00
  • Basics of Deep Learning: Problem to Solve Part 22:26
  • Basics of Deep Learning: Problem to Solve Part 31:42
  • Basics of Deep Learning: Linear Equation3:18
  • Basics of Deep Learning: Linear Equation Vectorized3:00
  • Basics of Deep Learning: 3D Feature Space3:46
  • Basics of Deep Learning: N Dimensional Space2:30
  • Basics of Deep Learning: Theory of Perceptron1:45
  • Basics of Deep Learning: Implementing Basic Perceptron5:37
  • Basics of Deep Learning: Logical Gates for Perceptrons2:46
  • Basics of Deep Learning: Perceptron Training Part 11:39
  • Basics of Deep Learning: Perceptron Training Part 23:40
  • Basics of Deep Learning: Learning Rate3:14
  • Basics of Deep Learning: Perceptron Training Part 33:31
  • Basics of Deep Learning: Perceptron Algorithm1:00
  • Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)5:51
  • Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)7:22
  • Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)6:42
  • Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)3:53
  • Basics of Deep Learning: Problem with Linear Solutions2:32
  • Basics of Deep Learning: Solution to Problem1:03
  • Basics of Deep Learning: Error Functions2:21
  • Basics of Deep Learning: Discrete vs Continuous Error Function2:25
  • Basics of Deep Learning: Sigmoid Function3:01
  • Basics of Deep Learning: Multi-Class Problem1:17
  • Basics of Deep Learning: Problem of Negative Scores3:02
  • Basics of Deep Learning: Need of Softmax1:22
  • Basics of Deep Learning: Coding Softmax4:05
  • Basics of Deep Learning: One Hot Encoding2:40
  • Basics of Deep Learning: Maximum Likelihood Part 15:30
  • Basics of Deep Learning: Maximum Likelihood Part 23:47
  • Basics of Deep Learning: Cross Entropy4:06
  • Basics of Deep Learning: Cross Entropy Formulation7:38
  • Basics of Deep Learning: Multi Class Cross Entropy3:51
  • Basics of Deep Learning: Cross Entropy Implementation4:14
  • Basics of Deep Learning: Sigmoid Function Implementation0:57
  • Basics of Deep Learning: Output Function Implementation2:10
  • Deep Learning: Introduction to Gradient Decent5:21
  • Deep Learning: Convex Functions2:31
  • Deep Learning: Use of Derivatives3:12
  • Deep Learning: How Gradient Decent Works3:34
  • Deep Learning: Gradient Step1:54
  • Deep Learning: Logistic Regression Algorithm1:37
  • Deep Learning: Data Visualization and Reading6:10
  • Deep Learning: Updating Weights in Python4:14
  • Deep Learning: Implementing Logistic Regression12:44
  • Deep Learning: Visualization and Results8:43
  • Deep Learning: Gradient Decent vs Perceptron4:35
  • Deep Learning: Linear to Non Linear Boundaries4:42
  • Deep Learning: Combining Probabilities2:07
  • Deep Learning: Weighted Sums3:01
  • Deep Learning: Neural Network Architecture12:09
  • Deep Learning: Layers and DEEP Networks4:44
  • Deep Learning: Multi Class Classification2:48
  • Deep Learning: Basics of Feed Forward7:50
  • Deep Learning: Feed Forward for DEEP Net4:57
  • Deep Learning: Deep Learning Algo Overview1:57
  • Deep Learning: Basics of Back Propagation6:32
  • Deep Learning: Updating Weights2:46
  • Deep Learning: Chain Rule for BackPropagation5:53
  • Deep Learning: Sigma Prime2:23
  • Deep Learning: Data Analysis NN Implementation5:25
  • Deep Learning: One Hot Encoding (NN Implementation)3:11
  • Deep Learning: Scaling the Data (NN Implementation)1:47
  • Deep Learning: Splitting the Data (NN Implementation)4:55
  • Deep Learning: Helper Functions (NN Implementation)2:18
  • Deep Learning: Training (NN Implementation)12:25
  • Deep Learning: Testing (NN Implementation)3:21
  • Optimizations: Underfitting vs Overfitting5:19
  • Optimizations: Early Stopping3:51
  • Optimizations: Quiz0:58
  • Optimizations: Solution & Regularization5:59
  • Optimizations: L1 & L2 Regularization3:12
  • Optimizations: Dropout2:59
  • Optimizations: Local Minima Problem2:55
  • Optimizations: Random Restart Solution4:27
  • Optimizations: Vanishing Gradient Problem4:16
  • Optimizations: Other Activation Functions3:19
  • Final Project: Final Project Part 111:19
  • Final Project: Final Project Part 213:16
  • Final Project: Final Project Part 312:58
  • Final Project: Final Project Part 412:19
  • Final Project: Final Project Part 58:06

Requirements

  • • No prior knowledge of Deep Learning or Math is needed. You will start from the basics and build your knowledge of the subject step by step.
  • • Basic understanding of Python programming.
  • No prior experience with TensorFlow is required, but a basic understanding of machine learning concepts and Python will be helpful.

Description

Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow—the most powerful libraries and frameworks for building intelligent models.

Whether you're a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you'll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.

Why Choose This Course?

This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you'll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.

Key Highlights:

  • Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.

  • PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.

  • TensorFlow: Unlock TensorFlow's potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.

  • Real-world Projects: Apply your knowledge to exciting projects like IRIS classification, brain tumor detection from MRI images, and more.

  • Data Preprocessing & ML Concepts: Learn crucial data preprocessing techniques and key machine learning principles such as Gradient Descent, Back Propagation, and Model Optimization.

Course Content Overview:

Module 1: Introduction to Deep Learning and Python

  • Introduction to the course structure, learning objectives, and key frameworks.

  • Overview of Python programming: from basics to advanced, ensuring you can confidently implement any deep learning concept.

Module 2: Deep Neural Networks (DNNs) with Python and NumPy

  • Programming with Python and NumPy: Understand arrays, data frames, and data preprocessing techniques.

  • Building DNNs from scratch using NumPy.

  • Implementing machine learning algorithms, including Gradient Descent, Logistic Regression, Feed Forward, and Back Propagation.

Module 3: Deep Learning with PyTorch

  • Learn about tensors and their importance in deep learning.

  • Perform operations on tensors and understand autograd for automatic differentiation.

  • Build basic and complex neural networks with PyTorch.

  • Implement CNNs for advanced image recognition tasks.

  • Final Project: Brain Tumor Detection using MRI Images.

Module 4: Mastering TensorFlow for Deep Learning

  • Dive into TensorFlow and understand its core features.

  • Build your first deep learning model using TensorFlow, starting with a simple neuron and progressing to Artificial Neural Networks (ANNs).

  • TensorFlow Playground: Experiment with various models and visualize performance.

  • Explore advanced deep learning projects, learning concepts like gradient descent, epochs, backpropagation, and model evaluation.

Who Should Take This Course?

  • Aspiring Data Scientists and Machine Learning Enthusiasts eager to develop deep expertise in neural networks.

  • Software Developers looking to expand their skillset with PyTorch and TensorFlow.

  • Business Analysts and AI Enthusiasts interested in applying deep learning to real-world problems.

  • Anyone passionate about learning how deep learning can drive innovation across industries, from healthcare to autonomous driving.

What You’ll Learn:

  • Programming with Python, NumPy, and Pandas for data manipulation and model development.

  • How to build and train Deep Neural Networks and Convolutional Neural Networks using PyTorch and TensorFlow.

  • Practical deep learning applications like brain tumor detection and IRIS classification.

  • Key machine learning concepts, including Gradient Descent, Model Optimization, and more.

  • How to preprocess and handle data efficiently using tools like DataLoader in PyTorch and Transforms for data augmentation.

Hands-on Experience:

By the end of this course, you will not only have learned the theory but will also have built multiple deep learning models, gaining hands-on experience in real-world projects.

Who this course is for:

  • • Anyone interested in Data Science.
  • • People who want to master DNNs with real datasets in Deep Learning.
  • • People who want to implement DNNs in realistic projects.
  • • Software developers and data scientists looking to expand their skillset with PyTorch.
  • • Beginners who want to enter the field of deep learning and artificial intelligence.
  • • Anyone Curious About Deep Learning and TensorFlow

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