The Deep Learning & Artificial Intelligence Course Bundle – 91% OFF

Deal Valid Till 1 Jan 1970

Deep Learning Prerequisites: Linear Regression in Python

Take the Foot Steps Into Deep Learning by using Probability Theory to Make More Accurate Predictions with Linear Regression
Number of Lessons : 20
Duration of Content : 02 hours
Profound Learning is an arrangement of capable calculations that are the constrain behind self-driving autos, picture searching, voice acknowledgment, and numerous, numerous more applications we consider quite “advanced.” One of the focal establishments of profound learning is straight relapse; utilizing likelihood hypothesis to increase further knowledge into the “line of best fit.” This is the initial step to building machines that, basically, demonstration like neurons in a neural system as they learn while they’re encouraged more data. In this course, you’ll begin with the rudiments of building a straight relapse module in Python, and advance into useful machine learning issues that will give the establishments to an investigation of Deep Learning.

  1. Get to 20 video lectures and 2 hours of substance day in and day out
  2. Utilize a 1-D direct relapse to demonstrate Moore’s Law
  3. Learn how to make a machine learning model that can learn from numerous data sources
  4. Apply multi-dimensional direct relapse to foresee a patient’s systolic circulatory strain given their age and weight
  5. Talk about speculation, over-fitting, prepare test parts, and different issues that may emerge while performing information investigation

 

Deep Learning Prerequisites: Logistic Regression in Python

Introduce Yourself to the Building Blocks of Neural Networks with Python Logistic Regression.
Number of Lessons : 31
Duration of Content : 03 hours
Strategic relapse is a standout among the most essential strategies utilized as a part of machine learning, information science, and insights, as it might be utilized to make a grouping or naming calculation that very looks like a natural neuron. Calculated relapse units, by expansion, are the essential blocks in the neural system, the focal design in profound learning. In this course, you’ll deal with calculated relapse utilizing handy, certifiable cases to completely value the endless uses of Deep Learning.

  1. Get to 31 video lectures and 3 hours of substance day in and day out
  2. Code your own calculated relapse module in Python
  3. Finish a course project that predicts client activities on a website given client information
  4. Utilize Deep Learning for outward appearance acknowledgment
  5. See how to settle on information driven choices

 

Data Science: Deep Learning in Python

Learn Artificial Neural Networks That Make Google Seem to Know Everything
Number of Lessons : 37
Duration of Content : 04 hours
Counterfeit neural systems are the design that make Apple’s Siri perceive your voice, Tesla’s self-driving autos know where to turn, Google Translate learn new dialects, thus numerous more mechanical components you have conceivably underestimated. The information science that joins every one of them is Deep Learning. In this course, you’ll build your first neural system, going past fundamental models to build arranges that consequently learn highlights.

  1. Get to 37 video lectures and 4 hours of substance all day, every day
  2. Extend the twofold arrangement model to different classes uing the softmax work
  3. Code the imperative training technique, backpropagation, in Numpy
  4. Execute a neural system utilizing Google’s TensorFlow library
  5. Foresee client activities on a website given client information utilizing a neural system
  6. Utilize Deep Learning for outward appearance acknowledgment
  7. Learn a portion of the most up to date development in neural systems

 

Data Science: Practical Deep Learning in Theano & TensorFlow

Learn and Understand Neural Networks by Using the 2 Most Popular Deep Learning Techniques Theano & TensorFlow
Number of Lessons : 23
Duration of Content : 03 hours
The uses of Deep Learning are numerous, and continually developing, much the same as the neural systems that it underpins. In this course, you’ll dig into cutting edge ideas of Deep Learning, beginning with the essentials of TensorFlow and Theano, seeing how to build neural systems with these well known tools. Utilizing these tools, you’ll learn how to build and comprehend a neural system, knowing precisely how to imagine what is occurring inside a model as it learns.

  1. Get to 23 video lectures and 3 hours of programming day in and day out
  2. Find group and stochastic slope plunge, two systems that permit you to prepare on a little specimen of information at every emphasis, incredibly accelerating training time
  3. Talk about how energy can bring you through neighborhood minima
  4. Learn versatile learning rate strategies like AdaGrad and RMSprop
  5. Investigate dropout regularization and other current neural system procedures
  6. Comprehend the factors and articulations of TensorFlow and Theano
  7. Set up a GPU-case on AWS and analyze the speed of CPU versus GPU for training a profound neural system
  8. Take a gander at the MNIST dataset and think about against known benchmarks